This paper proposes an approach for object class localization which goes beyond bounding boxes , as it also determines the outline of the object . Unlike most current localization methods , our approach does not require any hypothesis parameter space to be defined . Instead , it directly generates , evaluates and clusters shape masks . Thus , the presented framework produces more informative results for object class localization . For example , it easily learns and detects possible object viewpoints and articulations , which are often well characterized by the object outline . On the other hand , there exhibit large variations in visual appearance and structures even within the same kind of visual pattern , which makes visual pattern discovery a very challenging task . However , since to distinguish different kinds of visual patterns from each other is a fundamental problem of many tasks in computer vision , such as pattern recognition/classification , object detection/localization , content-based image search , many studies have been introduce to solve the problem of visual pattern discovery in the literature . In this paper , we will revisit the representative studies on discovering visual patterns and discuss these methods from the view of local-feature-based and object- proposal-based visual patterns . We propose a novel method called Contour-Aware Regions detector ( CAR ) to find representative regions in images . Inspired by the recent research conclusion of general object proposal methods that contour is important in object localization , we first alleviate the problem of super pixle overlapping multi-object regions . And then perceptual regions are generated during the merging process using the data structure similar to MSER . The design has the ability to sense asynchronous signals transmitted from electronic markers such as flashing light emitting diodes ( LEDs ) tagged on moving robots . With the presented sensor , object localization and position detection functions are simplified , performed at higher speeds in real time , and power requirement is reduced . The sensor in-pixel processing , filters out the background image data and detects the modulated marker regions . In contrast to current approaches which mostly rely on specialized multiprocessor architectures for fast processing , we use a distributed network architecture to support parallelism and attain real-time performance . In this paper , a new approach to linage matching is proposed as the basis of object localization and positioning , which involves dynamic texture feature extraction and hierarchical image matching . A mask based stochastic method is introduced to extract feature points for matching . Specifically , we integrate non-local modules into an existing base network at both low- and high-level layers . Our final model , named non-local combinational class activation maps ( NL-CCAM ) , obtains superior performance compared to previous methods on representative object localization benchmarks including ILSVRC 2016 and CUB- 200 - 2011 . Furthermore , we show that the proposed method has a great capability of generalization by visualizing other datasets . In this paper , we focus on the object localization problem in images given a single hand-drawn example as the object model . We propose a novel framework for shape-based object detection and recognition , which we formulate as a graph-based search problem . Abstract Random bin picking is often considered primarily to be an object localization problem . However , the determination of a suitable grasp in a given situation is an equally important part of picking chaotically stored objects for which only few solutions exist . For that purpose active partitions , a generalization of active contour approach , was used with superpixel representation of image content . Active partitions , similarly to active contours , possess a natural ability to incorporate external experience into object localization process . It means that not only information contained in the image itself but also experience of the radiologist and the medical knowledge can be used to improve segmentation results . The system can be made to operate in either a spatial or an object attention mode by introducing a top-down bias to either the dorsal or the ventral stream modules . In this system , translation invariant object recognition and object spatial localization arise from the interaction among the modules , with the early visual areas playing a key role in mediating such interaction . The quality of life of many people could be improved by autonomous humanoid robots in the home . To function in the human world , a humanoid household robot must be able to locate itself and perceive the environment like a human ; scene perception , object detection and segmentation , and object spatial localization in 3D are fundamental capabilities for such humanoid robots . This paper presents a 3D multi-class object detection and segmentation method . Our system can interface with conventional image recognition methods for efficient scene authoring , enabling objects in an image to be robustly identified using cheap cameras , minimal computations , and no domain knowledge . We demonstrate several applications of our system , including , photo-browsing , e-commerce , augmented reality , and objection localization . section : INTRODUCTION Convolutional Neural Networks ( CNNs ) have recently pushed the state of the art on large - scale tasks in many domains dealing with natural data , most notably in computer vision tasks like image classification , object detection , object localization and segmentation . Early applications of neural nets to speech recognition used Time - Delay Neural Nets which can be seen as simple forms of CNNs without pooling or subsampling . section : Introduction Since the 2012 ImageNet competition winning entry by Krizhevsky et al , their network “ AlexNet ” has been successfully applied to a larger variety of computer vision tasks , for example to object - detection , segmentation , human pose estimation , video classification , object tracking , and superresolution . These successes spurred a new line of research that focused on finding higher performing convolutional neural networks . Besides our methodological innovations , we critically review the AP metric used in KITTI3D , which emerged as the most important dataset for comparing 3D detection results . We identify and resolve a flaw in the 11-point interpolated AP metric , affecting all previously published detection results and particularly biases the results of monocular 3D detection . We provide extensive experimental evaluations and ablation studies and set a new state-of-the-art on the KITTI3D Car class . Generally , the combination of expensive LiDAR sensors and stereo RGB imaging has been paramount for successful 3D object detection algorithms , whereas monocular image-only methods experience drastically reduced performance . We propose to reduce the gap by reformulating the monocular 3D detection problem as a standalone 3D region proposal network . We leverage the geometric relationship of 2D and 3D perspectives , allowing 3D boxes to utilize well-known and powerful convolutional features generated in the image-space . To help address the strenuous 3D parameter estimations , we further design depth-aware convolutional layers which enable location specific feature development and in consequence improved 3D scene understanding . Compared to prior work in monocular 3D detection , our method consists of only the proposed 3D region proposal network rather than relying on external networks , data , or multiple stages . M3D-RPN is able to significantly improve the performance of both monocular 3D Object Detection and Bird 's Eye View tasks within the KITTI urban autonomous driving dataset , while efficiently using a shared multi-class model . While expensive LiDAR and stereo camera rigs have enabled the development of successful 3D object detection methods , monocular RGB-only approaches lag much behind . This work advances the state of the art by introducing MoVi-3D , a novel , single-stage deep architecture for monocular 3D object detection . MoVi-3D builds upon a novel approach which leverages geometrical information to generate , both at training and test time , virtual views where the object appearance is normalized with respect to distance . Monocular 3D object detection is an essential component in autonomous driving while challenging to solve , especially for those occluded samples which are only partially visible . Most detectors consider each 3D object as an independent training target , inevitably resulting in a lack of useful information for occluded samples . In both domains we demonstrate the strength and versatility of the proposed method . A new approach to searching global solution by first finding a set of local solutionsApplied to both monocular 3D pose detection and multiview 3D trackingAchieve good accuracy using weak likelihood model and no assumed motion modelSource code is publically available to ensure reproducible results . These representations can recover geometrically far more accurate object hypotheses than just bounding boxes , including continuous estimates of object pose and 3D wireframes with relative 3D positions of object parts . In combination with robust techniques for shape description and inference , we outperform state-of-the-art results in monocular 3D pose estimation . In a series of experiments , we analyze our approach in detail and demonstrate novel applications enabled by such an object class representation , such as fine-grained categorization of cars and bicycles , according to their 3D geometry , and ultrawide baseline matching . Most recent approaches to monocular 3D pose estimation rely on Deep Learning . They either train a Convolutional Neural Network to directly regress from image to 3D pose , which ignores the dependencies between human joints , or model these dependencies via a max-margin structured learning framework , which involves a high computational cost at inference time . While recent systems have shown that this is best accomplished with a 3D scene model , handling of partial object occlusion is still unsatisfactory . In this paper we propose an approach that tightly integrates monocular 3D scene tracking-by-detection with explicit object-object occlusion reasoning . Full object and object part detectors are combined in a mixture of experts based on their expected visibility , which is obtained from the 3D scene model . This paper proposes a real-time , robust and efficient 3D model-based tracking algorithm for visual servoing . A virtual visual servoing approach is used for monocular 3D tracking . This method is similar to more classical nonlinear pose computation techniques . This paper proposes a real-time , robust and efficient 3D model-based tracking algorithm . A nonlinear minimization approach is used to register 2D and 3D cues for monocular 3D tracking . The integration of texture information in a more classical nonlinear edge-based pose computation highly increases the reliability of more conventional edge-based 3D tracker . This work is concerned with the matching of straight lines between two stereo image pairs by reprojection . While we will focus on visual odometry in the realm of simultaneous mapping and localization , the techniques are also relevant to monocular and stereo 3D object detection and tracking . Our first contribution is an adaptation of the Iterative Closest Point ( ICP ) algorithm to the domain of lines . In this work , we make a number of new contributions both in computer vision and machine learning . We address the vision problems of shape matching , shape-based mining of objects in arbitrary image collections , context-aware object recognition , monocular estimation of 3D object poses , and monocular 3D scene reconstruction using shape from texture . Our work on shape-based object discovery is the first to show that meaningful objects can be extracted from a collection of arbitrary images , without any human supervision , by shape matching . In his famous thesis , Ullman noted that the theory of structure from motion fails to explain many psychological apparent motion phenomena , and that the motion-from-structure process must be included in any complete theory of motion understanding . Any system for monocular tracking of known 3D objects must solve the motion-from-structure problem . As the name suggests , tnolion-from-structure assumes that object shape is known and that relative object motion in space is to be computed . Supervised object localization requires detailed image annotation , such as bounding box , to indicate the location of the object . However , labeling image with bounding box is labor-intensive . We introduce novel techniques that allow this approach to be deployed in the setting of autoencoders and show that this method can be used for unsupervised group-wise image alignment . We show experiments with expression morphing in humans , hands , and digits , face manipulation , such as shape and appearance interpolation , as well as unsupervised landmark localization . A more powerful form of unsupervised disentangling becomes possible in template coordinates , allowing us to successfully decompose face images into shading and albedo , and further manipulate face images . The reading process of visual codes consists of two steps : localization and data decoding . Unsupervised localization is desirable at industrial setups and for visually impaired people . This paper examines localization efficiency of cascade classifiers using Haar-like features , Local Binary Patterns and Histograms of Oriented Gradients , trained for the finder patterns of QR codes and for the whole code region as well , and proposes improvements in post-processing . The reading process of visual codes consists of two tasks : localization and data decoding . Unsupervised localization is desirable at industrial setups and for visually impaired people . This paper examines localization efficiency of cascade classifiers using Haarlike features , Local Binary Patterns and Histograms of Oriented Gradients , trained for the finder patterns of QR codes and for the whole code region as well , and proposes improvements in post-processing . In this paper we address the problem of unsupervised localization of objects in single images . Compared to previous state-of-the-art method our method is fully unsupervised in the sense that there is no prior instance level or category level information about the image . We start with local affine features selected and normalized independently in training and test images , and jointly expand their domain as part of the correspondence process , akin to a ( non-rigid ) registration task that yields a ( multi-view ) segmentation of the object of interest from clutter , including the detection of occlusions . We show how our growth process can be used to validate putative affine matches , to match a given " template " ( an image of an object without clutter ) to a cluttered and partially occluded image , and to match two images that contain the same unknown object in different clutter under different occlusions ( unsupervised object detection ) . Indeed , recent machine learning literature is replete with examples of the benefits of object-like representations : generalization , transfer to new tasks , and interpretability , among others . However , in order to reason in terms of objects , agents need a way of discovering and detecting objects in the visual world - a task which we call unsupervised object detection . This task has received significantly less attention in the literature than its supervised counterpart , especially in the case of large images containing many objects . In the current work , we develop a neural network architecture that effectively addresses this large-image , many-object setting . In particular , we combine ideas from Attend , Infer , Repeat ( AIR ) , which performs unsupervised object detection but does not scale well , with recent developments in supervised object detection . We replace AIR ’s core recurrent network with a convolutional ( and thus spatially invariant ) network , and make use of an object-specification scheme that describes the location of objects with respect to local grid cells rather than the image as a whole . The goal of our work is to discover dominant objects without using any annotations . We focus on performing unsupervised object discovery and localization in a very general setting where only a single image is given . This is far more challenge than typical co-localization or weakly-supervised localization tasks . Unsupervised object discovery and localization is to discover some dominant object classes and localize all of object instances from a given image collection without any supervision . Previous work has attempted to tackle this problem with vanilla topic models , such as latent Dirichlet allocation ( LDA ) . In such cases , model transfer would dramatically improve the performance by evading much expensive labelling efforts . Towards this end , this paper focuses on model transferring for solving the unsupervised object localization problem . We propose an effective method for utilizing the pre-trained deep convolutional models to evaluate the correlation of features and predict results between original image and sub-images which generated via object proposal . Five papers that brought important contributions to the field are summarized , analyzed and compared . It was found that unsupervised object recognition was considered first as an image segmentation problem , but new unsupervised object learning techniques have been developed requiring no image segmentation at all . In this report , we however stipulate that the latter techniques could possibly benefit from unsupervised image segmentation to provide even better unsupervised object recognition . The objective of this paper is to study the existing methods for unsupervised object recognition and image categorization and propose a model that can learn directly from the output of image search engines , e.g. Google Images , bypassing the need to manually collect large quantities of training data . This model can then be used to refine the quality of the image search , or to search through other sources of images . Both steps are repeated alternately , within interactive response times , until the segmentation refinement of the final frame is accepted by the user . Extensive experiments demonstrate FOMTrace 's ability for tracing objects in comparison with state-of-the-art approaches for interactive video segmentation , supervised , and unsupervised object tracking . section : Classification Results While WSDDN is primarily designed for weaklysupervised object detection , ultimately it is trained to perform image classification . Hence , it is interesting to evaluate its performance on this task as well . WS - DDN S and M improves 8 and 7 points over VGG - F and VGG - M - 1024 respectively . We also compare WSDDN to the SPP - net [ reference ] which uses the Overfeat - 7 [ reference ] with a 4 - level spatial pyramid pooling layer { 6 × 6 , 3 × 3 , 2 × 2 , 1 × 1 } for supervised object detection . While they do not perform fine - tuning , they include a spatial pooling layer . We tackle image super - resolution as a clustering - regression problem , and focus on the clustering stage , which happens at the split - nodes of each decision tree . By employing the JMPF strategy , we rotate the feature space into a pre - clustered feature space , which can cluster samples into different sub - spaces more compactly in an unsupervised problem . The compact pre - clustered feature space can provide the optimal thresholds for split - nodes in decision trees , which are the zero - centered orthogonal hyperplanes . Along this track , most previous methods employ traditional machine learning techniques and construct classifiers based on geometric features . In the domain of unsupervised shape segmentation , there is one family of methods emphasizes the effectiveness of spectral analysis for 3D shape segmentation . Inspired by this , our framework aims to marry the powerfulness of deep neural network and spectral analysis for 3D shapes segmentation . paragraph : Benefits of fertility Fertilities possess all three of the properties listed earlier as desired of a latent variable for non - autoregressive machine translation : An external aligner provides a simple and fast approximate inference model that effectively reduces the unsupervised training problem to two supervised ones . Using fertilities as a latent variable makes significant progress towards solving the multimodality problem by providing a natural factorization of the output space . This is in fact a “ global pooling ” operation , which is also investigated in several concurrent works . In a global average pooling is used to reduce the model size and also reduce overfitting ; in , a global average pooling is used on the testing stage after all fc layers to improve accuracy ; in , a global max pooling is used for weakly supervised object recognition . The global pooling operation corresponds to the traditional Bag - of - Words method . Virtual reality , augmented reality , robotics , and autonomous driving , have recently attracted much attention from both academic and industrial communities , in which image-based camera localization is a key task . However , there has not been a complete review on image-based camera localization . It is urgent to map this topic to enable individuals enter the field quickly . It is a known fact that GPS systems used for localizing vehicle can not be activated in some cases and this inability has accelerated image based positioning studies . In our study , we performed image based localization using dataset that includes Malaga city center images . Firstly , a semantic descriptor is obtained as a result of semantic segmentation and localization was performed using the approximate nearest neighbor search . In the domain of image based localization , alternatives to image feature based algorithms have been developed making use of similarity metrics that operate directly on pixel intensity values . These approaches can work robustly and efficiently in cases when the recognition process needs to deal for example with varying lighting conditions or changes in scenery detail . Image-based localization for mobile and vehicular applications Hamed Sadeghi Doctor of Philosophy Graduate Department of Electrical and Computer Engineering University of Toronto 2017 We study the image-based localization problem in mobile and vehicular applications . The problem can be stated as ‘ given a query image at an unknown location with some side information available regarding the camera and environment , how can one estimate the camera location ? We seek to predict the GPS location of a query image given a database of images localized on a map with known GPS locations . The contributions of this work are three-fold : ( 1 ) we formulate the image-based localization problem as a regression on an image graph with images as nodes and edges connecting close-by images ; ( 2 ) we design a novel image matching procedure , which computes similarity between the query and pairs of database images using edges of the graph and considering linear combinations of their feature vectors . This improves generalization to unseen viewpoints and illumination conditions , while reducing the database size ; ( 3 ) we demonstrate that the query location can be predicted by interpolating locations of matched images in the graph without the costly estimation of multi-view geometry . We discuss the advantage of using an exact image Jacobian with respect to the dynamic behaviour of the servoing process . From an experimental point of view , we describe a grasping experiment involving image-based object localization , grasp planning , and visual servoing . In coarse localization , the proposed MS2ML calls the Bayesian vision-motion topological localization to obtain a set of nodes from a visual map . Furthermore , the holistic feature is generated for each query image , and hence , the holistic feature matching is implemented to realize image-level localization . A node is then selected from the candidate nodes . The proposed method has been tested in an indoor office building of 12 000 m2 and a mega-mall of 7200 m2 with different types of smartphones . The experimental results demonstrate that the proposed method can achieve 95 % and 98 % site recognition rates from image-level localization . The final localization errors after metric localization are less than a half meter on average . We consider four representative approaches : two novel approaches ( based on Gaussian process dynamical models and probabilistic hierarchical trajectory matching ) that use augmented features derived from dense optical flow and two approaches as baseline that use positional information only ( a Kalman filter and its extension to interacting multiple models ) . In experiments using stereo vision data obtained from a vehicle , we investigate the accuracy of path prediction and action classification at various time horizons , the effect of various errors ( image localization , vehicle egomotion estimation ) , and the benefit of the proposed approaches . The scenario of interest is that of a crossing pedestrian , who might stop or continue walking at the road curbside . The Levenshtein distance also provides matches between imaged points , from which their corresponding environment points can be reconstructed . Image matching with the aid of the Levenshtein distance forms the crux of an iterative process that alternates between image localization from multiple reconstructed points and point reconstruction from multiple image projections , until all views have been localized . Periodic refinement of the reconstruction with the aid of bundle adjustment , distributes the reconstruction error among images . However , recent crowdsourcing tasks have become more complicated and usually consist of real-valued vectors . In this paper , we propose a novel inference algorithm for vector regression tasks which ask workers to provide accurate vectors such as image object localization and human posture estimation . Our algorithm can estimate the true answer of each task and a reliability of each worker by updating two types of messages iteratively . However , multilayer LIDARs such as Velodyne HDL 64E are expensive and not applicable in consuming products . On the other hand , image sensor based localization relies on feature point matching and is sensitive to the change of weather and illumination . In this paper , we proposed a monocular vision based localization method within a prior map built with LIDAR . The results of our research show the viability of single image localization with absolute position accuracies at the decimetre level for outdoor environments and 5 cm or better for indoor environments . However , the evaluation also revealed a number of limitations of single image visual localization in real-world environments . Some of them could be addressed by an alternative AR-based localization approach , which we also present and compare in this paper .
This paper proposes an approach for object class localization which goes beyond bounding boxes , as it also determines the outline of the object . Unlike most current localization methods , our approach does not require any hypothesis parameter space to be defined . Instead , it directly generates , evaluates and clusters shape masks . Thus , the presented framework produces more informative results for object class localization . For example , it easily learns and detects possible object viewpoints and articulations , which are often well characterized by the object outline .
On the other hand , there exhibit large variations in visual appearance and structures even within the same kind of visual pattern , which makes visual pattern discovery a very challenging task . However , since to distinguish different kinds of visual patterns from each other is a fundamental problem of many tasks in computer vision , such as pattern recognition/classification , object detection/localization , content-based image search , many studies have been introduce to solve the problem of visual pattern discovery in the literature . In this paper , we will revisit the representative studies on discovering visual patterns and discuss these methods from the view of local-feature-based and object- proposal-based visual patterns .
We propose a novel method called Contour-Aware Regions detector ( CAR ) to find representative regions in images . Inspired by the recent research conclusion of general object proposal methods that contour is important in object localization , we first alleviate the problem of super pixle overlapping multi-object regions . And then perceptual regions are generated during the merging process using the data structure similar to MSER .
The design has the ability to sense asynchronous signals transmitted from electronic markers such as flashing light emitting diodes ( LEDs ) tagged on moving robots . With the presented sensor , object localization and position detection functions are simplified , performed at higher speeds in real time , and power requirement is reduced . The sensor in-pixel processing , filters out the background image data and detects the modulated marker regions .
In contrast to current approaches which mostly rely on specialized multiprocessor architectures for fast processing , we use a distributed network architecture to support parallelism and attain real-time performance . In this paper , a new approach to linage matching is proposed as the basis of object localization and positioning , which involves dynamic texture feature extraction and hierarchical image matching . A mask based stochastic method is introduced to extract feature points for matching .
Specifically , we integrate non-local modules into an existing base network at both low- and high-level layers . Our final model , named non-local combinational class activation maps ( NL-CCAM ) , obtains superior performance compared to previous methods on representative object localization benchmarks including ILSVRC 2016 and CUB- 200 - 2011 . Furthermore , we show that the proposed method has a great capability of generalization by visualizing other datasets .
In this paper , we focus on the object localization problem in images given a single hand-drawn example as the object model . We propose a novel framework for shape-based object detection and recognition , which we formulate as a graph-based search problem .
Abstract Random bin picking is often considered primarily to be an object localization problem . However , the determination of a suitable grasp in a given situation is an equally important part of picking chaotically stored objects for which only few solutions exist .
For that purpose active partitions , a generalization of active contour approach , was used with superpixel representation of image content . Active partitions , similarly to active contours , possess a natural ability to incorporate external experience into object localization process . It means that not only information contained in the image itself but also experience of the radiologist and the medical knowledge can be used to improve segmentation results .
The system can be made to operate in either a spatial or an object attention mode by introducing a top-down bias to either the dorsal or the ventral stream modules . In this system , translation invariant object recognition and object spatial localization arise from the interaction among the modules , with the early visual areas playing a key role in mediating such interaction .
The quality of life of many people could be improved by autonomous humanoid robots in the home . To function in the human world , a humanoid household robot must be able to locate itself and perceive the environment like a human ; scene perception , object detection and segmentation , and object spatial localization in 3D are fundamental capabilities for such humanoid robots . This paper presents a 3D multi-class object detection and segmentation method .
Our system can interface with conventional image recognition methods for efficient scene authoring , enabling objects in an image to be robustly identified using cheap cameras , minimal computations , and no domain knowledge . We demonstrate several applications of our system , including , photo-browsing , e-commerce , augmented reality , and objection localization .
section : INTRODUCTION Convolutional Neural Networks ( CNNs ) have recently pushed the state of the art on large - scale tasks in many domains dealing with natural data , most notably in computer vision tasks like image classification , object detection , object localization and segmentation . Early applications of neural nets to speech recognition used Time - Delay Neural Nets which can be seen as simple forms of CNNs without pooling or subsampling .
section : Introduction Since the 2012 ImageNet competition winning entry by Krizhevsky et al , their network “ AlexNet ” has been successfully applied to a larger variety of computer vision tasks , for example to object - detection , segmentation , human pose estimation , video classification , object tracking , and superresolution . These successes spurred a new line of research that focused on finding higher performing convolutional neural networks .
Besides our methodological innovations , we critically review the AP metric used in KITTI3D , which emerged as the most important dataset for comparing 3D detection results . We identify and resolve a flaw in the 11-point interpolated AP metric , affecting all previously published detection results and particularly biases the results of monocular 3D detection . We provide extensive experimental evaluations and ablation studies and set a new state-of-the-art on the KITTI3D Car class .
Generally , the combination of expensive LiDAR sensors and stereo RGB imaging has been paramount for successful 3D object detection algorithms , whereas monocular image-only methods experience drastically reduced performance . We propose to reduce the gap by reformulating the monocular 3D detection problem as a standalone 3D region proposal network . We leverage the geometric relationship of 2D and 3D perspectives , allowing 3D boxes to utilize well-known and powerful convolutional features generated in the image-space . To help address the strenuous 3D parameter estimations , we further design depth-aware convolutional layers which enable location specific feature development and in consequence improved 3D scene understanding . Compared to prior work in monocular 3D detection , our method consists of only the proposed 3D region proposal network rather than relying on external networks , data , or multiple stages . M3D-RPN is able to significantly improve the performance of both monocular 3D Object Detection and Bird 's Eye View tasks within the KITTI urban autonomous driving dataset , while efficiently using a shared multi-class model .
While expensive LiDAR and stereo camera rigs have enabled the development of successful 3D object detection methods , monocular RGB-only approaches lag much behind . This work advances the state of the art by introducing MoVi-3D , a novel , single-stage deep architecture for monocular 3D object detection . MoVi-3D builds upon a novel approach which leverages geometrical information to generate , both at training and test time , virtual views where the object appearance is normalized with respect to distance .
Monocular 3D object detection is an essential component in autonomous driving while challenging to solve , especially for those occluded samples which are only partially visible . Most detectors consider each 3D object as an independent training target , inevitably resulting in a lack of useful information for occluded samples .
In both domains we demonstrate the strength and versatility of the proposed method . A new approach to searching global solution by first finding a set of local solutionsApplied to both monocular 3D pose detection and multiview 3D trackingAchieve good accuracy using weak likelihood model and no assumed motion modelSource code is publically available to ensure reproducible results .
These representations can recover geometrically far more accurate object hypotheses than just bounding boxes , including continuous estimates of object pose and 3D wireframes with relative 3D positions of object parts . In combination with robust techniques for shape description and inference , we outperform state-of-the-art results in monocular 3D pose estimation . In a series of experiments , we analyze our approach in detail and demonstrate novel applications enabled by such an object class representation , such as fine-grained categorization of cars and bicycles , according to their 3D geometry , and ultrawide baseline matching .
Most recent approaches to monocular 3D pose estimation rely on Deep Learning . They either train a Convolutional Neural Network to directly regress from image to 3D pose , which ignores the dependencies between human joints , or model these dependencies via a max-margin structured learning framework , which involves a high computational cost at inference time .
While recent systems have shown that this is best accomplished with a 3D scene model , handling of partial object occlusion is still unsatisfactory . In this paper we propose an approach that tightly integrates monocular 3D scene tracking-by-detection with explicit object-object occlusion reasoning . Full object and object part detectors are combined in a mixture of experts based on their expected visibility , which is obtained from the 3D scene model .
This paper proposes a real-time , robust and efficient 3D model-based tracking algorithm for visual servoing . A virtual visual servoing approach is used for monocular 3D tracking . This method is similar to more classical nonlinear pose computation techniques .
This paper proposes a real-time , robust and efficient 3D model-based tracking algorithm . A nonlinear minimization approach is used to register 2D and 3D cues for monocular 3D tracking . The integration of texture information in a more classical nonlinear edge-based pose computation highly increases the reliability of more conventional edge-based 3D tracker .
This work is concerned with the matching of straight lines between two stereo image pairs by reprojection . While we will focus on visual odometry in the realm of simultaneous mapping and localization , the techniques are also relevant to monocular and stereo 3D object detection and tracking . Our first contribution is an adaptation of the Iterative Closest Point ( ICP ) algorithm to the domain of lines .
In this work , we make a number of new contributions both in computer vision and machine learning . We address the vision problems of shape matching , shape-based mining of objects in arbitrary image collections , context-aware object recognition , monocular estimation of 3D object poses , and monocular 3D scene reconstruction using shape from texture . Our work on shape-based object discovery is the first to show that meaningful objects can be extracted from a collection of arbitrary images , without any human supervision , by shape matching .
In his famous thesis , Ullman noted that the theory of structure from motion fails to explain many psychological apparent motion phenomena , and that the motion-from-structure process must be included in any complete theory of motion understanding . Any system for monocular tracking of known 3D objects must solve the motion-from-structure problem . As the name suggests , tnolion-from-structure assumes that object shape is known and that relative object motion in space is to be computed .
Supervised object localization requires detailed image annotation , such as bounding box , to indicate the location of the object . However , labeling image with bounding box is labor-intensive .
We introduce novel techniques that allow this approach to be deployed in the setting of autoencoders and show that this method can be used for unsupervised group-wise image alignment . We show experiments with expression morphing in humans , hands , and digits , face manipulation , such as shape and appearance interpolation , as well as unsupervised landmark localization . A more powerful form of unsupervised disentangling becomes possible in template coordinates , allowing us to successfully decompose face images into shading and albedo , and further manipulate face images .
The reading process of visual codes consists of two steps : localization and data decoding . Unsupervised localization is desirable at industrial setups and for visually impaired people . This paper examines localization efficiency of cascade classifiers using Haar-like features , Local Binary Patterns and Histograms of Oriented Gradients , trained for the finder patterns of QR codes and for the whole code region as well , and proposes improvements in post-processing .
The reading process of visual codes consists of two tasks : localization and data decoding . Unsupervised localization is desirable at industrial setups and for visually impaired people . This paper examines localization efficiency of cascade classifiers using Haarlike features , Local Binary Patterns and Histograms of Oriented Gradients , trained for the finder patterns of QR codes and for the whole code region as well , and proposes improvements in post-processing .
In this paper we address the problem of unsupervised localization of objects in single images . Compared to previous state-of-the-art method our method is fully unsupervised in the sense that there is no prior instance level or category level information about the image .
We start with local affine features selected and normalized independently in training and test images , and jointly expand their domain as part of the correspondence process , akin to a ( non-rigid ) registration task that yields a ( multi-view ) segmentation of the object of interest from clutter , including the detection of occlusions . We show how our growth process can be used to validate putative affine matches , to match a given " template " ( an image of an object without clutter ) to a cluttered and partially occluded image , and to match two images that contain the same unknown object in different clutter under different occlusions ( unsupervised object detection ) .
Indeed , recent machine learning literature is replete with examples of the benefits of object-like representations : generalization , transfer to new tasks , and interpretability , among others . However , in order to reason in terms of objects , agents need a way of discovering and detecting objects in the visual world - a task which we call unsupervised object detection . This task has received significantly less attention in the literature than its supervised counterpart , especially in the case of large images containing many objects . In the current work , we develop a neural network architecture that effectively addresses this large-image , many-object setting . In particular , we combine ideas from Attend , Infer , Repeat ( AIR ) , which performs unsupervised object detection but does not scale well , with recent developments in supervised object detection . We replace AIR ’s core recurrent network with a convolutional ( and thus spatially invariant ) network , and make use of an object-specification scheme that describes the location of objects with respect to local grid cells rather than the image as a whole .
The goal of our work is to discover dominant objects without using any annotations . We focus on performing unsupervised object discovery and localization in a very general setting where only a single image is given . This is far more challenge than typical co-localization or weakly-supervised localization tasks .
Unsupervised object discovery and localization is to discover some dominant object classes and localize all of object instances from a given image collection without any supervision . Previous work has attempted to tackle this problem with vanilla topic models , such as latent Dirichlet allocation ( LDA ) .
In such cases , model transfer would dramatically improve the performance by evading much expensive labelling efforts . Towards this end , this paper focuses on model transferring for solving the unsupervised object localization problem . We propose an effective method for utilizing the pre-trained deep convolutional models to evaluate the correlation of features and predict results between original image and sub-images which generated via object proposal .
Five papers that brought important contributions to the field are summarized , analyzed and compared . It was found that unsupervised object recognition was considered first as an image segmentation problem , but new unsupervised object learning techniques have been developed requiring no image segmentation at all . In this report , we however stipulate that the latter techniques could possibly benefit from unsupervised image segmentation to provide even better unsupervised object recognition .
The objective of this paper is to study the existing methods for unsupervised object recognition and image categorization and propose a model that can learn directly from the output of image search engines , e.g. Google Images , bypassing the need to manually collect large quantities of training data . This model can then be used to refine the quality of the image search , or to search through other sources of images .
Both steps are repeated alternately , within interactive response times , until the segmentation refinement of the final frame is accepted by the user . Extensive experiments demonstrate FOMTrace 's ability for tracing objects in comparison with state-of-the-art approaches for interactive video segmentation , supervised , and unsupervised object tracking .
section : Classification Results While WSDDN is primarily designed for weaklysupervised object detection , ultimately it is trained to perform image classification . Hence , it is interesting to evaluate its performance on this task as well .
WS - DDN S and M improves 8 and 7 points over VGG - F and VGG - M - 1024 respectively . We also compare WSDDN to the SPP - net [ reference ] which uses the Overfeat - 7 [ reference ] with a 4 - level spatial pyramid pooling layer { 6 × 6 , 3 × 3 , 2 × 2 , 1 × 1 } for supervised object detection . While they do not perform fine - tuning , they include a spatial pooling layer .
We tackle image super - resolution as a clustering - regression problem , and focus on the clustering stage , which happens at the split - nodes of each decision tree . By employing the JMPF strategy , we rotate the feature space into a pre - clustered feature space , which can cluster samples into different sub - spaces more compactly in an unsupervised problem . The compact pre - clustered feature space can provide the optimal thresholds for split - nodes in decision trees , which are the zero - centered orthogonal hyperplanes .
Along this track , most previous methods employ traditional machine learning techniques and construct classifiers based on geometric features . In the domain of unsupervised shape segmentation , there is one family of methods emphasizes the effectiveness of spectral analysis for 3D shape segmentation . Inspired by this , our framework aims to marry the powerfulness of deep neural network and spectral analysis for 3D shapes segmentation .
paragraph : Benefits of fertility Fertilities possess all three of the properties listed earlier as desired of a latent variable for non - autoregressive machine translation : An external aligner provides a simple and fast approximate inference model that effectively reduces the unsupervised training problem to two supervised ones . Using fertilities as a latent variable makes significant progress towards solving the multimodality problem by providing a natural factorization of the output space .
This is in fact a “ global pooling ” operation , which is also investigated in several concurrent works . In a global average pooling is used to reduce the model size and also reduce overfitting ; in , a global average pooling is used on the testing stage after all fc layers to improve accuracy ; in , a global max pooling is used for weakly supervised object recognition . The global pooling operation corresponds to the traditional Bag - of - Words method .
Virtual reality , augmented reality , robotics , and autonomous driving , have recently attracted much attention from both academic and industrial communities , in which image-based camera localization is a key task . However , there has not been a complete review on image-based camera localization . It is urgent to map this topic to enable individuals enter the field quickly .
It is a known fact that GPS systems used for localizing vehicle can not be activated in some cases and this inability has accelerated image based positioning studies . In our study , we performed image based localization using dataset that includes Malaga city center images . Firstly , a semantic descriptor is obtained as a result of semantic segmentation and localization was performed using the approximate nearest neighbor search .
In the domain of image based localization , alternatives to image feature based algorithms have been developed making use of similarity metrics that operate directly on pixel intensity values . These approaches can work robustly and efficiently in cases when the recognition process needs to deal for example with varying lighting conditions or changes in scenery detail .
Image-based localization for mobile and vehicular applications Hamed Sadeghi Doctor of Philosophy Graduate Department of Electrical and Computer Engineering University of Toronto 2017 We study the image-based localization problem in mobile and vehicular applications . The problem can be stated as ‘ given a query image at an unknown location with some side information available regarding the camera and environment , how can one estimate the camera location ?
We seek to predict the GPS location of a query image given a database of images localized on a map with known GPS locations . The contributions of this work are three-fold : ( 1 ) we formulate the image-based localization problem as a regression on an image graph with images as nodes and edges connecting close-by images ; ( 2 ) we design a novel image matching procedure , which computes similarity between the query and pairs of database images using edges of the graph and considering linear combinations of their feature vectors . This improves generalization to unseen viewpoints and illumination conditions , while reducing the database size ; ( 3 ) we demonstrate that the query location can be predicted by interpolating locations of matched images in the graph without the costly estimation of multi-view geometry .
We discuss the advantage of using an exact image Jacobian with respect to the dynamic behaviour of the servoing process . From an experimental point of view , we describe a grasping experiment involving image-based object localization , grasp planning , and visual servoing .
In coarse localization , the proposed MS2ML calls the Bayesian vision-motion topological localization to obtain a set of nodes from a visual map . Furthermore , the holistic feature is generated for each query image , and hence , the holistic feature matching is implemented to realize image-level localization . A node is then selected from the candidate nodes .
The proposed method has been tested in an indoor office building of 12 000 m2 and a mega-mall of 7200 m2 with different types of smartphones . The experimental results demonstrate that the proposed method can achieve 95 % and 98 % site recognition rates from image-level localization . The final localization errors after metric localization are less than a half meter on average .
We consider four representative approaches : two novel approaches ( based on Gaussian process dynamical models and probabilistic hierarchical trajectory matching ) that use augmented features derived from dense optical flow and two approaches as baseline that use positional information only ( a Kalman filter and its extension to interacting multiple models ) . In experiments using stereo vision data obtained from a vehicle , we investigate the accuracy of path prediction and action classification at various time horizons , the effect of various errors ( image localization , vehicle egomotion estimation ) , and the benefit of the proposed approaches . The scenario of interest is that of a crossing pedestrian , who might stop or continue walking at the road curbside .
The Levenshtein distance also provides matches between imaged points , from which their corresponding environment points can be reconstructed . Image matching with the aid of the Levenshtein distance forms the crux of an iterative process that alternates between image localization from multiple reconstructed points and point reconstruction from multiple image projections , until all views have been localized . Periodic refinement of the reconstruction with the aid of bundle adjustment , distributes the reconstruction error among images .
However , recent crowdsourcing tasks have become more complicated and usually consist of real-valued vectors . In this paper , we propose a novel inference algorithm for vector regression tasks which ask workers to provide accurate vectors such as image object localization and human posture estimation . Our algorithm can estimate the true answer of each task and a reliability of each worker by updating two types of messages iteratively .
However , multilayer LIDARs such as Velodyne HDL 64E are expensive and not applicable in consuming products . On the other hand , image sensor based localization relies on feature point matching and is sensitive to the change of weather and illumination . In this paper , we proposed a monocular vision based localization method within a prior map built with LIDAR .
The results of our research show the viability of single image localization with absolute position accuracies at the decimetre level for outdoor environments and 5 cm or better for indoor environments . However , the evaluation also revealed a number of limitations of single image visual localization in real-world environments . Some of them could be addressed by an alternative AR-based localization approach , which we also present and compare in this paper .
Accurate Object Localization with Shape Masks Common visual pattern discovery and search Efficient Perceptual Region Detector Based on Object Boundary An electronic marker tag detection smart camera for object localization Real-Time Textured Object Recognition on Distributed Systems Combinational Class Activation Maps for Weakly Supervised Object Localization Efficient graph-based search for object detection Gripping Point Determination for Bin Picking Using Heuristic Search Knowledge Based Active Partition Approach for Heart Ventricle Recognition A unified model of spatial and object attention based on inter-cortical biased competition Multi-Class Detection and Segmentation of Objects in Depth Capturing Images with Sparse Informational Pixels using Projected 3D Tags Very deep multilingual convolutional neural networks for LVCSR Rethinking the Inception Architecture for Computer Vision Disentangling Monocular 3D Object Detection M3D-RPN: Monocular 3D Region Proposal Network for Object Detection Towards Generalization Across Depth for Monocular 3D Object Detection. MonoPair: Monocular 3D Object Detection Using Pairwise Spatial Relationships Fixing the root node: Efficient tracking and detection of 3D human pose through local solutions Detailed 3D Representations for Object Recognition and Modeling Structured Prediction of 3D Human Pose with Deep Neural Networks Monocular 3D scene understanding with explicit occlusion reasoning Robust model-based tracking for robot vision Real-time 3D model-based tracking: combining edge and texture information Robust stereo visual odometry using iterative closest multiple lines From shape-based object recognition and discovery to 3d scene interpretation Perspective Alignment Back Projection for Monocular Tracking of Solid Objects Weakly supervised object localization with deep convolutional neural network based on spatial pyramid saliency map Deforming Autoencoders: Unsupervised Disentangling of Shape and Appearance QR Code Localization Using Boosted Cascade of Weak Classifiers Improved QR Code Localization Using Boosted Cascade of Weak Classifiers Fusing Saliency Maps with Region Proposals for Unsupervised Object Localization Local Features, All Grown Up Spatially Invariant Unsupervised Object Detection with Convolutional Neural Networks Mining Objects: Fully Unsupervised Object Discovery and Localization From a Single Image Knowledge-Based Topic Model for Unsupervised Object Discovery and Localization Model transferring for unsupervised object localization Unsupervised Learning for Object Recognition Unsupervised Categorization (Filtering) of Google Images Based on Visual Consistency FOMTrace: Interactive Video Segmentation By Image Graphs and Fuzzy Object Models Weakly Supervised Deep Detection Networks Joint maximum purity forest with application to image super-resolution SyncSpecCNN: Synchronized Spectral CNN for 3D Shape Segmentation Non-Autoregressive Neural Machine Translation Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition Image-based camera localization: an overview Image Based Localization Using Semantic Segmentation for Autonomous Driving Implementation and Evaluation of Image Sequence Based Place Recognition Utilizing a Humanoid Robot Image-based localization for mobile and vehicular applications Visual localization by linear combination of image descriptors Integrating Grasp Planning and Visual Servoing for Automatic Grasping Multiscale Site Matching for Vision-Only Self-Localization of Intelligent Vehicles WiFi and Vision-Integrated Fingerprint for Smartphone-Based Self-Localization in Public Indoor Scenes Will the Pedestrian Cross? A Study on Pedestrian Path Prediction Horizon matching for localizing unordered panoramic images Reliable Aggregation Method for Vector Regression Tasks in Crowdsourcing Monocular Localization Within Manually Annotated LIDAR Maps ACCURATE VISUAL LOCALIZATION IN OUTDOOR AND INDOOR ENVIRONMENTS EXPLOITING 3D IMAGE SPACES AS SPATIAL REFERENCE
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CVPR APSIPA MMM (2) ICSC CORES VR 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) BMVC CVPR IROS ICRA IROS BMVC ICIP ICIAR (1) CVPR (2) AAAI CIS/RAM AAAI 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) J. Electronic Imaging 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ICLR IEEE Transactions on Pattern Analysis and Machine Intelligence SIU ICCV Workshops ISER PAKDD (2)
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