an exemplary embodiment of the present invention will now be described with respect to the figures , and an example of the operation of the embodiment given . fig1 illustrates an example system architecture which provides the embodiment of the invention . more particularly , as the present invention generally relates to an image processing technique for detecting objects within input images , the invention is primarily embodied as software to be run on a computer . therefore , the system architecture of the present invention comprises a general purpose computer 16 , as is well known in the art . the computer 16 is provided with a display 20 on which output images generated by the computer may be displayed to a user , and is further provided with various user input devices 18 , such as keyboards , mice , or the like . the general purpose computer 16 is also provided with a data storage medium 22 such as a hard disk , memory , optical disk , or the like , upon which is stored programs and data . more specifically , on the data storage medium 22 are stored data 24 corresponding to an adaptive background image , data 28 corresponding to an input image , and data 30 corresponding to working data such as image data , segmentation maps , or the like used as intermediate storage during the operation of the invention . additionally stored on the data storage medium 22 is executable program code in the form of programs such as the control program 31 , a background adaptation program 32 , a foreground segmentation program 36 , a connected component program 26 , a shadow detection program 34 , and a blob reconstruction program 38 . the operation of each of these programs will be described in turn later . the computer 16 is arranged to receive images from an image capture device 12 , such as a camera or the like . the image capture device 12 may be connected directly to the computer 16 , or alternatively may be logically connected to the computer 16 via a network 14 such as the internet . the image capture device 12 is arranged to provide sequential video images of a scene in which objects are to be detected , the video images being composed of picture elements ( pixels ) which take particular values so as to have particular luminance and chrominance characteristics . the colour model used for the pixels output from the image capture device 12 may be any known in the art e . g . rgb , yuv , etc . in operation , the general purpose computer 16 receives images from the image capture device 12 via the network , or directly , and then runs the various programs stored on the data storage medium 22 so as to process the received input image in order to detect objects therein . a more detailed description of the operation of the embodiment will now be undertaken with respect to fig2 . fig2 illustrates a flow diagram of the operation of the embodiment of the invention . starting at step 2 . 2 , here the general purpose computer 16 receives an input image to be analysed using the invention from the image from the image capture device 12 , either via the network 14 , or directly . the embodiment of the invention then acts to analyse the contents of the received input image so as to detect foreground objects of interest therein . within this embodiment , foreground objects are taken to be moving objects , or other objects of interest . the process of detecting foreground objects relies on effectively segmenting ( i . e . distinguishing ) the pixels representing the foreground objects from pixels representing the background of the scene in the image , and then performing a connected component analysis on the segmented foreground images in order to characterise them as objects . the general purpose computer operates to process the received image under the control of the control program 31 . that is , the control program 31 controls the order of processing to be applied to received input images , and runs the other programs to perform their respective processing operations in turn , passing input data to them and receiving processed data from them as appropriate . in view of this , the first processing to be performed once the input image has been received is to perform a segmentation of those images that do not belong to the background scene . within the present embodiment the adaptive background subtraction method proposed by stauffer and grimson in “ learning patterns of activity using real time tracking ”, ieee trans on pattern analysis and machine intelligence , 22 ( 8 ), august 2000 was adopted . here , a mixture of k gaussian distributions is used to model the colour changes , at each pixel location , in the imaged scene over time . with each incoming frame the gaussian distributions are updated , and then used to determine which pixels are most likely to result from a background process . this model allows a proper representation of the background scene undergoing slow lighting and scene changes , but not momentary variations such as swaying trees or flags in the wind . in view of the above , within the present embodiment at step 2 . 4 foreground pixels are segmented from the input image 28 , using the stauffer and grimson technique , and at step 2 . 6 the adaptive background image 24 is updated to take into account the received input image , and the updated image stored in the data storage medium 22 . full details of the necessary procedures to undertake step 2 . 4 are described in stauffer and grimson , ibid . sections 3 . 1 and 3 . 2 . regarding step 2 . 6 , the background pixels are obtained as follows : the pixel colours in the background image assume those of the incoming image if they are classified as background at step 2 . 4 . in the case that the incoming pixels have been classified as foreground , then the mean of the gaussian distribution with the largest weight at the lowest variance ( the most probable background colour in the pixel ) is chosen as the background pixel colour . as a result an updated background image can be obtained for every incoming frame . within the embodiment the segmentation step 2 . 4 is performed by the foreground segmentation program 36 , and the background adaptation step 2 . 6 is performed by the background adaptation program 32 , both under the control of the control program 31 . an example background image from the sequence of input images used to generate the example images shown herein is shown in fig5 . an example input image with the shadow cast by an object circled is shown in fig6 . the result of the segmentation performed at step 2 . 4 is that a map of segmented pixels is obtained , as shown in fig8 , for the example input image depicted in fig7 . the foreground pixels thus obtained , however , are not exempt from false detections due to noise in the background or from a shaking camera . hence , at step 2 . 8 a false foreground pixel suppression procedure is introduced to alleviate this problem . here , when a pixel is initially classified by the segmentation performed at step 2 . 6 as a foreground pixel , its eight connected neighbouring pixels models are examined . if the majority of these models , when applied to this pixel , agree that it is a background pixel , then it is considered as a false detection , and removed from the foreground segmentation . this “ false foreground ” test is applied to every pixel which was segmented at step 2 . 4 using the stauffer and grimson technique , and hence the segmentation as foreground for each of these pixels is validated . following the “ false foreground ” validation , the map of segmented pixels as shown for example in fig8 is stored at step 2 . 10 , for use as a “ mask ” of segmented pixels later in the process . this map of segmented pixels is stored in the working data area 30 of the data storage medium 22 . once the foreground pixels have been identified , a further detection technique is applied to find out if some of these foreground pixels correspond to regions likely to be cast shadows or highlights . as explained in horprasert et al “ a statistical approach for real time robust background subtraction and shadow detection ”, iccv &# 39 ; 99 frame rate workshop at section 4 . 2 , a pixel can be considered a shadow pixel if it has similar chromaticity to , but lower brightness than , those of the same pixel in the background image , or can be considered as a highlight pixel if it has similar chromaticity but higher brightness than the corresponding pixel in the background image . this characteristic of shadow or highlight pixels is referred to as “ colour constancy ” in that the chromaticity of the shadow or highlight pixels in an input image is similar to or identical to the corresponding pixel in the background image , with the only difference being in the luminance of the input image pixels . therefore , as a first step in detecting shadows and / or highlights at step 2 . 12 the colour distortion (“ cd ”), and brightness distortion (“ bd ”) is calculated as described by horprasert et al and as shown in fig3 herein , between respective foreground pixels and the corresponding pixels of the adaptive background image stored at step 2 . 6 , and possible shadows are detected . more particularly , at step 2 . 12 aggressive thresholds are used for the colour and brightness distortion , and in particular the following logical conditions are applied :— in other embodiments of the invention the thresholds may differ from those given above ; the precise values of the thresholds should be found by calibration of any particular implementation . those segmented pixels which are identified as either shadow or highlight pixels by the above logical conditions are then marked for removal from the foreground segmentation map . note that in the embodiment the above steps are performed by the shadow detection program 34 , under the control of the control program 31 . although the above colour constancy test is effective in detecting shadows , it was observed that the procedure was less effective in cases where the objects of interest have similar colours to that of presumed shadows . to correct this , a second process comparing the gradients / textures of the regions of the segmented pixels is also used , in a similar manner to that described by mckenna et al at section 3 . 2 of their paper “ tracking groups of people ”, referenced above . more particularly , at step 2 . 14 within the embodiment of the invention the shadow detection program 34 acts to calculate the spatial gradients of those pixels of the input image which are marked as segmented according to the present segmentation map , and then compares the calculated gradients with correspondingly calculated gradients of the respective corresponding pixels from the adaptive background , as described in detail by mckenna et al in section 3 . 2 of their paper . the segmented pixels of the input image which are found to have a similar gradient to the adaptive background are then marked as candidates for removal from the foreground segmentation map . the result of the operations of the steps 2 . 12 and step 2 . 14 are that both shadow and highlight pixels are marked for removal from the segmentation map , but at this stage no segmented pixels have yet been removed from the segmentation map . at step 2 . 16 , therefore , the shadow detection program 34 identifies those pixels which were identified at both steps 2 . 12 and 2 . 14 as being candidates for shadow or highlight pixels , and removes those pixels from the segmentation map . as a consequence , those pixels which exhibited both colour and gradient similarity with the background are detected as shadows or highlights , and removed from the object segmentation . as an aside , although within the preferred embodiment a pixel is only removed from the segmentation at step 2 . 16 if it was marked as a potential shadow / highlight pixel by both steps 2 . 12 and 2 . 14 , in alternative embodiments this need not be the case , and a pixel may be removed from segmentation if either one or the other , or both , of the tests performed at steps 2 . 12 and 2 . 14 indicates the pixel to be a shadow / highlight pixel . this may result in even more pixels being identified as shadow / highlight pixels and being removed from the foreground segmentation than in the preferred embodiment where both tests must be met . returning to the main embodiment , because of the aggressive application of the shadow removal techniques ( due to the thresholds chosen for the logical conditions ) it will also be found that many segmented pixels which properly represent the objects of interest will also have been removed from the segmentation map . an example segmentation map produced by the application of steps 2 . 12 to 2 . 16 is shown in fig9 from where it will be seen , with comparison to the corresponding fig8 which illustrates the segmentation map prior to the performance of the steps , that the segmented pixels representing the objects of interest are much reduced , and exhibit both holes and bisections within and of the group of segmented pixels representing the objects . however , it should also be noted that the segmented pixels which represent shadow pixels , which are very prominent in fig8 , have been completely removed within fig9 . as will be seen from fig9 , however , the map of segmented pixels stored at step 2 . 16 contains a speckle of erroneously segmented pixels therein , and hence at step 2 . 17 speckle noise removal on the segmentation map is performed in order to suppress isolated noisy foreground pixels . this speckle noise removal technique is performed using a morphological operator as shown in equation 1 : where n is the structuring element shown in fig4 . from fig4 , it will be seen that n is a 3 × 3 structuring element comprising the four - connected neighbours of the origin , but that the origin is not included . additionally , the term ( m ⊕ n ) in equation 1 identifies all the pixels that are four - connected to , i . e . next to , a pixel of m . hence , { tilde over ( m )} identifies all the pixels that are in m and also have a four - connected neighbour and , therefore , these points correspond to all the points in m except the isolated ones . the resulting speckle free segmentation map resulting from this speckle filtering step is then stored in the working data area 30 of the storage medium 22 . if a connected component analysis to find objects were to be performed on the blobs of fig9 following shadow removal , then due to the various holes and bisections within the groups of pixels various erroneous objects would be detected . therefore , the embodiment of the invention provides that the various holes and bisections in the segmented blobs of pixels are removed and this is performed at step 2 . 18 by the repeated application of a morphological dilation operation to the segmented pixels in the segmentation map , to segment surrounding pixels to the presently segmented pixels . this morphological dilation operation is repeatedly performed until the segmented blobs touch or just overlap the boundary of the corresponding respective blob of segmented pixels produced from the segmentation at step 2 . 4 ( i . e . fig8 ), whereupon the dilation operation is then stopped . the effect of this is to “ grow ” the segmented blobs left over after the shadow removal process so as to fill in any holes or bisections within the blobs of segmented pixels . more specifically , at step 2 . 18 , a dilation operation using a 9 × 9 block structuring element ( with each block of the element being set to “ 1 ”) is performed on the foreground segmented pixels of the segmentation map corresponding to fig9 by the blob reconstruction program 38 to segment surrounding pixels to the already segmented pixels as foreground pixels . then , at step 2 . 20 , the area of segmented blobs in the segmentation map thus obtained is compared with the segmented blobs within the stored mask , being the segmentation map obtained from the segmentation step of step 2 . 4 , i . e . the stauffer and grimson segmentation . at step 2 . 22 , an evaluation is performed to determine whether any of the segmented blobs obtained as a result of the morphological dilation touch or overlap the segmented blobs of the mask segmentation map . if this evaluation determines that there is no touching or overlapping of the mask segmentation blobs , then processing returns in a loop to the morphological dilation step 2 . 18 which is performed again . the loop comprising the steps 2 . 18 , 2 . 20 , and 2 . 22 is repeatedly performed until the evaluation of step 2 . 22 returns positive , in which case the loop is then ended . formally , therefore , segmented blobs within the segmentation map are reconstructed according to equation 2 : where m s the mask image obtained from the segmentation of step 2 . 10 , { tilde over ( m )} the segmented pixels after the shadow / highlight removal steps of s . 2 . 12 to 2 . 16 , and se the structuring element whose size usually depends on the size of the objects ; as mentioned above a 9 × 9 square element proved to work well in all our tests , but other size structuring elements which result in a dilation may be used . the underlying idea of this morphological dilation step is that the shadow removed blobs keep at least a number of points that have been robust to wrong shadow removal . these robust points are appropriate for leading the reconstruction of neighbouring points as long as they form part of the silhouette in the original blob obtained from the segmentation of step 2 . 4 . the resulting segmentation map output by the blob reconstruction program 38 once the processing loop has ended should resemble the example shown in fig1 from which it will be seen when compared to fig9 that the segmented pixel blobs have been reconstructed , and any holes or bisections therein have been removed . it will also be seen , moreover , that the reconstructed segmented pixel blobs of fig1 do not contain any substantial shadow pixels within the segmentation . the segmented pixel blobs thus obtained in fig1 can therefore be used as input to a classical eight connectivity connected component analysis algorithm to link all the pixels presumably belonging to one object into a detected object , respectively . thus , at step 2 . 24 a connected component analysis is performed on the segmentation map obtained from the processing loop by the connected component program 26 , in order to detect objects from the segmented blobs contained within the segmentation map . data identifying the detected objects is then stored within the working data area 30 of the storage medium 22 , and may be used subsequently as an input to an object classification application , an object tracking application , or the like . alternatively , the embodiment of the invention may be used on its own solely to detect objects , and to display the objects on the display 20 to a user . in other embodiments , a further action may be performed if one or more objects are detected in the input image , such as , for example , the computer 16 causing an output device to give an audio or visual indication of the detection of an object to a user . the embodiment of the invention is therefore particularly suitable for use in monitoring applications and the like . unless the context clearly requires otherwise , throughout the description and the claims , the words “ comprise ”, “ comprising ” and the like are to be construed in an inclusive as opposed to an exclusive or exhaustive sense ; that is to say , in the sense of “ including , but not limited to ”.