Improved GrabCut segmentation via GMM optimisation

Semi-automatic segmentation of still images has vast and varied practical applications. Recently, an approach "GrabCut" has managed to successfully build upon earlier approaches based on colour and gradient information in order to address the problem of efficient extraction of a foreground object in a complex environment. In this paper, we extend the GrabCut algorithm further by applying an unsupervised algorithm for modelling the Gaussian Mixtures that are used to define the foreground and background in the segmentation algorithm. We show examples where the optimisation of the GrabCut framework leads to further improvements in performance.

Design of automatic vision-based inspection system for solder joint segmentation

Purpose: Computer vision has been widely used in the inspection of electronic components. This paper proposes a computer vision system for the automatic detection, localisation, and segmentation of solder joints on Printed Circuit Boards (PCBs) under different illumination conditions. Design/methodology/approach: An illumination normalization approach is applied to an image, which can effectively and efficiently eliminate the effect of uneven illumination while keeping the properties of the processed image the same as in the corresponding image under normal lighting conditions. Consequently special lighting and instrumental setup can be reduced in order to detect solder joints. These normalised images are insensitive to illumination variations and are used for the subsequent solder joint detection stages. In the segmentation approach, the PCB image is transformed from an RGB color space to a YIQ color space for the effective detection of solder joints from the background. Findings: The segmentation results show that the proposed approach improves the performance significantly for images under varying illumination conditions. Research limitations/implications: This paper proposes a front-end system for the automatic detection, localisation, and segmentation of solder joint defects. Further research is required to complete the full system including the classification of solder joint defects. Practical implications: The methodology presented in this paper can be an effective method to reduce cost and improve quality in production of PCBs in the manufacturing industry. Originality/value: This research proposes the automatic location, identification and segmentation of solder joints under different illumination conditions.

Automatic solder joint defect classification using the Log-Gabor filter

This paper proposes the validity of a Gabor filter bank for feature extraction of solder joint images on Printed Circuit Boards (PCBs). A distance measure based on the Mahalanobis Cosine metric is also presented for classification of five different types of solder joints. From the experimental results, this methodology achieved high accuracy and a well generalised performance. This can be an effective method to reduce cost and improve quality in the production of PCBs in the manufacturing industry.

Road network extraction with new vectorization and pruning from high-resolution RS images

With the increasing resolution of remote sensing images, road network can be displayed as continuous and homogeneity regions with a certain width rather than traditional thin lines. Therefore, road network extraction from large scale images refers to reliable road surface detection instead of road line extraction. In this paper, a novel automatic road network detection approach based on the combination of homogram segmentation and mathematical morphology is proposed, which includes three main steps: (i) the image is classified based on homogram segmentation to roughly identify the road network regions; (ii) the morphological opening and closing is employed to fill tiny holes and filter out small road branches; and (iii) the extracted road surface is further thinned by a thinning approach, pruned by a proposed method and finally simplified with Douglas-Peucker algorithm. Lastly, the results from some QuickBird images and aerial photos demonstrate the correctness and efficiency of the proposed process.

Multi-sensor tracking using a scalable condensation filter

Surveillance and tracking systems typically use a single colour modality for their input. These systems work well in controlled conditions but often fail with low lighting, shadowing, smoke, dust, unstable backgrounds or when the foreground object is of similar colouring to the background. With advances in technology and manufacturing techniques, sensors that allow us to see into the thermal infrared spectrum are becoming more affordable. By using modalities from both the visible and thermal infrared spectra, we are able to obtain more information from a scene and overcome the problems associated with using visible light only for surveillance and tracking. Thermal images are not affected by lighting or shadowing and are not overtly affected by smoke, dust or unstable backgrounds. We propose and evaluate three approaches for fusing visual and thermal images for person tracking. We also propose a modified condensation filter to track and aid in the fusion of the modalities. We compare the proposed fusion schemes with using the visual and thermal domains on their own, and demonstrate that significant improvements can be achieved by using multiple modalities.

Multi-spectral fusion for surveillance systems

Surveillance systems such as object tracking and abandoned object detection systems typically rely on a single modality of colour video for their input. These systems work well in controlled conditions but often fail when low lighting, shadowing, smoke, dust or unstable backgrounds are present, or when the objects of interest are a similar colour to the background. Thermal images are not affected by lighting changes or shadowing, and are not overtly affected by smoke, dust or unstable backgrounds. However, thermal images lack colour information which makes distinguishing between different people or objects of interest within the same scene difficult. ----- By using modalities from both the visible and thermal infrared spectra, we are able to obtain more information from a scene and overcome the problems associated with using either modality individually. We evaluate four approaches for fusing visual and thermal images for use in a person tracking system (two early fusion methods, one mid fusion and one late fusion method), in order to determine the most appropriate method for fusing multiple modalities. We also evaluate two of these approaches for use in abandoned object detection, and propose an abandoned object detection routine that utilises multiple modalities. To aid in the tracking and fusion of the modalities we propose a modified condensation filter that can dynamically change the particle count and features used according to the needs of the system. ----- We compare tracking and abandoned object detection performance for the proposed fusion schemes and the visual and thermal domains on their own. Testing is conducted using the OTCBVS database to evaluate object tracking, and data captured in-house to evaluate the abandoned object detection. Our results show that significant improvement can be achieved, and that a middle fusion scheme is most effective.

Improved simultaneous computation of motion detection and optical flow for object tracking

Object tracking systems require accurate segmentation of the objects from the background for effective tracking. Motion segmentation or optical flow can be used to segment incoming images. Whilst optical flow allows multiple moving targets to be separated based on their individual velocities, optical flow techniques are prone to errors caused by changing lighting and occlusions, both common in a surveillance environment. Motion segmentation techniques are more robust to fluctuating lighting and occlusions, but don't provide information on the direction of the motion. In this paper we propose a combined motion segmentation/optical flow algorithm for use in object tracking. The proposed algorithm uses the motion segmentation results to inform the optical flow calculations and ensure that optical flow is only calculated in regions of motion, and improve the performance of the optical flow around the edge of moving objects. Optical flow is calculated at pixel resolution and tracking of flow vectors is employed to improve performance and detect discontinuities, which can indicate the location of overlaps between objects. The algorithm is evaluated by attempting to extract a moving target within the flow images, given expected horizontal and vertical movement (i.e. the algorithms intended use for object tracking). Results show that the proposed algorithm outperforms other widely used optical flow techniques for this surveillance application.

Unmanned Aerial Vehicles UAVs attitude, height, motion estimation and control using visual systems

This paper presents an implementation of an aircraft pose and motion estimator using visual systems as the principal sensor for controlling an Unmanned Aerial Vehicle (UAV) or as a redundant system for an Inertial Measure Unit (IMU) and gyros sensors. First, we explore the applications of the unified theory for central catadioptric cameras for attitude and heading estimation, explaining how the skyline is projected on the catadioptric image and how it is segmented and used to calculate the UAV’s attitude. Then we use appearance images to obtain a visual compass, and we calculate the relative rotation and heading of the aerial vehicle. Additionally, we show the use of a stereo system to calculate the aircraft height and to measure the UAV’s motion. Finally, we present a visual tracking system based on Fuzzy controllers working in both a UAV and a camera pan and tilt platform. Every part is tested using the UAV COLIBRI platform to validate the different approaches, which include comparison of the estimated data with the inertial values measured onboard the helicopter platform and the validation of the tracking schemes on real flights.

Labelled silhouettes for human pose estimation

This paper proposes a new method of using foreground silhouette images for human pose estimation. Labels are introduced to the silhouette images, providing an extra layer of information that can be used in the model fitting process. The pixels in the silhouettes are labelled according to the corresponding body part in the model of the current fit, with the labels propagated into the silhouette of the next frame to be used in the fitting for the next frame. Both single and multi-view implementations are detailed, with results showing performance improvements over only using standard unlabelled silhouettes.

Fusing shrinking and expanding active contour models for robust IRIS segmentation

The performance of iris recognition systems is significantly affected by the segmentation accuracy, especially in non- ideal iris images. This paper proposes an improved method to localise non-circular iris images quickly and accurately. Shrinking and expanding active contour methods are consolidated when localising inner and outer iris boundaries. First, the pupil region is roughly estimated based on histogram thresholding and morphological operations. There- after, a shrinking active contour model is used to precisely locate the inner iris boundary. Finally, the estimated inner iris boundary is used as an initial contour for an expanding active contour scheme to find the outer iris boundary. The proposed scheme is robust in finding exact the iris boundaries of non-circular and off-angle irises. In addition, occlusions of the iris images from eyelids and eyelashes are automatically excluded from the detected iris region. Experimental results on CASIA v3.0 iris databases indicate the accuracy of proposed technique.

Automatic tracking, super-resolution and recognition of human faces from surveillance video

Identifying an individual from surveillance video is a difficult, time consuming and labour intensive process. The proposed system aims to streamline this process by filtering out unwanted scenes and enhancing an individual's face through super-resolution. An automatic face recognition system is then used to identify the subject or present the human operator with likely matches from a database. A person tracker is used to speed up the subject detection and super-resolution process by tracking moving subjects and cropping a region of interest around the subject's face to reduce the number and size of the image frames to be super-resolved respectively. In this paper, experiments have been conducted to demonstrate how the optical flow super-resolution method used improves surveillance imagery for visual inspection as well as automatic face recognition on an Eigenface and Elastic Bunch Graph Matching system. The optical flow based method has also been benchmarked against the ``hallucination'' algorithm, interpolation methods and the original low-resolution images. Results show that both super-resolution algorithms improved recognition rates significantly. Although the hallucination method resulted in slightly higher recognition rates, the optical flow method produced less artifacts and more visually correct images suitable for human consumption.

Comparison of temporal processing and motion perception in emmetropes and myopes

While spatial determinants of emmetropization have been examined extensively in animal models and spatial processing of human myopes has also been studied, there have been few studies investigating temporal aspects of emmetropization and temporal processing in human myopia. The influence of temporal light modulation on eye growth and refractive compensation has been observed in animal models and there is evidence of temporal visual processing deficits in individuals with high myopia or other pathologies. Given this, the aims of this work were to examine the relationships between myopia (i.e. degree of myopia and progression status) and temporal visual performance and to consider any temporal processing deficits in terms of the parallel retinocortical pathways. Three psychophysical studies investigating temporal processing performance were conducted in young adult myopes and non-myopes: (1) backward visual masking, (2) dot motion perception and (3) phantom contour. For each experiment there were approximately 30 young emmetropes, 30 low myopes (myopia less than 5 D) and 30 high myopes (5 to 12 D). In the backward visual masking experiment, myopes were also classified according to their progression status (30 stable myopes and 30 progressing myopes). The first study was based on the observation that the visibility of a target is reduced by a second target, termed the mask, presented quickly after the first target. Myopes were more affected by the mask when the task was biased towards the magnocellular pathway; myopes had a 25% mean reduction in performance compared with emmetropes. However, there was no difference in the effect of the mask when the task was biased towards the parvocellular system. For all test conditions, there was no significant correlation between backward visual masking task performance and either the degree of myopia or myopia progression status. The dot motion perception study measured detection thresholds for the minimum displacement of moving dots, the maximum displacement of moving dots and degree of motion coherence required to correctly determine the direction of motion. The visual processing of these tasks is dominated by the magnocellular pathway. Compared with emmetropes, high myopes had reduced ability to detect the minimum displacement of moving dots for stimuli presented at the fovea (20% higher mean threshold) and possibly at the inferior nasal retina. The minimum displacement threshold was significantly and positively correlated to myopia magnitude and axial length, and significantly and negatively correlated with retinal thickness for the inferior nasal retina. The performance of emmetropes and myopes for all the other dot motion perception tasks were similar. In the phantom contour study, the highest temporal frequency of the flickering phantom pattern at which the contour was visible was determined. Myopes had significantly lower flicker detection limits (21.8 ± 7.1 Hz) than emmetropes (25.6 ± 8.8 Hz) for tasks biased towards the magnocellular pathway for both high (99%) and low (5%) contrast stimuli. There was no difference in flicker limits for a phantom contour task biased towards the parvocellular pathway. For all phantom contour tasks, there was no significant correlation between flicker detection thresholds and magnitude of myopia. Of the psychophysical temporal tasks studied here those primarily involving processing by the magnocellular pathway revealed differences in performance of the refractive error groups. While there are a number of interpretations for this data, this suggests that there may be a temporal processing deficit in some myopes that is selective for the magnocellular system. The minimum displacement dot motion perception task appears the most sensitive test, of those studied, for investigating changes in visual temporal processing in myopia. Data from the visual masking and phantom contour tasks suggest that the alterations to temporal processing occur at an early stage of myopia development. In addition, the link between increased minimum displacement threshold and decreasing retinal thickness suggests that there is a retinal component to the observed modifications in temporal processing.

Jack's Bay

Jack's Bay expands understandings of the role of photographic media in the representation of landscapes. It does so by combining architectural construction with B&W photographic processing techniques. A purpose-built room-sized camera obscura is first constructed over a portion of the landscape to be recorded. Photosensitive paper is applied to the interior wall surfaces and is exposed to the inverted light entering a small aperture. These photographs are subsequently developed within the camera itself and consequently 'suffer' embellishments and aberrations from the makeshift darkroom conditions. In this way the specificity of both the landscape and the event of its recording are registered in the final image. Many images were destroyed in the process. The idea of the work is to help the viewer reflect on the role media plays in our understanding of landscape and to thus question the means by which they themselves record and interpret landscape representations.

Corackerup Breakaway

Corackerup Breakaway expands understandings of the role of photographic media in the representation of landscapes. It does so by combining architectural construction with B&W photographic processing techniques. A purpose-built room-sized camera obscura is first constructed over a portion of the landscape to be recorded. Photosensitive paper is applied to the interior wall surfaces and is exposed to the inverted light entering a small aperture. These photographs are subsequently developed within the camera itself and consequently 'suffer' embellishments and aberrations from the makeshift darkroom conditions. In this way the specificity of both the landscape and the event of its recording are registered in the final image. Many images were destroyed in the process. The idea of the work is to help the viewer reflect on the role media plays in our understanding of landscape and to thus question the means by which they themselves record and interpret landscape representations.