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.

Robust real time multi-layer foreground segmentation

Many surveillance applications (object tracking, abandoned object detection) rely on detecting changes in a scene. Foreground segmentation is an effective way to extract the foreground from the scene, but these techniques cannot discriminate between objects that have temporarily stopped and those that are moving. We propose a series of modifications to an existing foreground segmentation system\cite{Butler2003} so that the foreground is further segmented into two or more layers. This yields an active layer of objects currently in motion and a passive layer of objects that have temporarily ceased motion which can itself be decomposed into multiple static layers. We also propose a variable threshold to cope with variable illumination, a feedback mechanism that allows an external process (i.e. surveillance system) to alter the motion detectors state, and a lighting compensation process and a shadow detector to reduce errors caused by lighting inconsistencies. The technique is demonstrated using outdoor surveillance footage, and is shown to be able to effectively deal with real world lighting conditions and overlapping objects.

Extraction of road lanes from high-resolution stereo aerial imagery based on maximum likelihood segmentation and texture enhancement

Accurate road lane information is crucial for advanced vehicle navigation and safety applications. With the increasing of very high resolution (VHR) imagery of astonishing quality provided by digital airborne sources, it will greatly facilitate the data acquisition and also significantly reduce the cost of data collection and updates if the road details can be automatically extracted from the aerial images. In this paper, we proposed an effective approach to detect road lanes from aerial images with employment of the image analysis procedures. This algorithm starts with constructing the (Digital Surface Model) DSM and true orthophotos from the stereo images. Next, a maximum likelihood clustering algorithm is used to separate road from other ground objects. After the detection of road surface, the road traffic and lane lines are further detected using texture enhancement and morphological operations. Finally, the generated road network is evaluated to test the performance of the proposed approach, in which the datasets provided by Queensland department of Main Roads are used. The experiment result proves the effectiveness of our approach.

Accurate silhouette segmentation using motion detection and graph cuts

Acquiring accurate silhouettes has many applications in computer vision. This is usually done through motion detection, or a simple background subtraction under highly controlled environments (i.e. chroma-key backgrounds). Lighting and contrast issues in typical outdoor or office environments make accurate segmentation very difficult in these scenes. In this paper, gradients are used in conjunction with intensity and colour to provide a robust segmentation of motion, after which graph cuts are utilised to refine the segmentation. The results presented using the ETISEO database demonstrate that an improved segmentation is achieved through the combined use of motion detection and graph cuts, particularly in complex scenes.

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.

De la reconstruction a l’augmentation du corps humain en medecine restaurative et en cybernetique

Aux confluences historiques et conceptuelles de la modernité, de la technologie, et de l’« humain », les textes de notre corpus négocient et interrogent de façon critique les possibilités matérielles et symboliques de la prothèse, ses aspects phénoménologiques et spéculatifs : du côté subjectiviste et conceptualiste avec une philosophie de la conscience, avec Merleau-Ponty ; et de l’autre avec les épistémologues du corps et historiens de la connaissance Canguilhem et Foucault. Le trope prometteur de la prothèse impacte sur les formations discursives et non-discursives concernant la reconstruction des corps, là où la technologie devient le corrélat de l’identité. La technologie s’humanise au contact de l’homme, et, en révélant une hybridité supérieure, elle phagocyte l’humain du même coup. Ce travail de sociologie des sciences (Latour, 1989), ou encore d’anthropologie des sciences (Hakken, 2001) ou d’anthropologie bioculturelle (Andrieu, 1993; Andrieu, 2006; Andrieu, 2007a) se propose en tant qu’exemple de la contribution potentielle que l’anthropologie biologique et culturelle peut rendre à la médecine reconstructrice et que la médecine reconstructrice peut rendre à la plastique de l’homme ; l’anthropologie biologique nous concerne dans la transformation biologique du corps humain, par l’outil de la technologie, tant dans son histoire de la reconstruction mécanique et plastique, que dans son projet d’augmentation bionique. Nous établirons une continuité archéologique, d’une terminologie foucaldienne, entre les deux pratiques. Nous questionnons les postulats au sujet des relations nature/culture, biologie/contexte social, et nous présentons une approche définitionnelle de la technologie, pierre angulaire de notre travail théorique. Le trope de la technologie, en tant qu’outil adaptatif de la culture au service de la nature, opère un glissement sémantique en se plaçant au service d’une biologie à améliorer. Une des clés de notre recherche sur l’augmentation des fonctions et de l’esthétique du corps humain réside dans la redéfinition même de ces relations ; et dans l’impact de l’interpénétration entre réalité et imaginaire dans la construction de l’objet scientifique, dans la transformation du corps humain. Afin de cerner les enjeux du discours au sujet de l’« autoévolution » des corps, les théories évolutionnistes sont abordées, bien que ne représentant pas notre spécialité. Dans le cadre de l’autoévolution, et de l’augmentation bionique de l’homme, la somation culturelle du corps s’exerce par l’usage des biotechnologies, en rupture épistémologique de la pensée darwinienne, bien que l’acte d’hybridation évolutionnaire soit toujours inscrit dans un dessein de maximisation bionique/génétique du corps humain. Nous explorons les courants de la pensée cybernétique dans leurs actions de transformation biologique du corps humain, de la performativité des mutilations. Ainsi technologie et techniques apparaissent-elles indissociables de la science, et de son constructionnisme social.

Accurate silhouettes for surveillance : improved motion segmentation using graph cuts

Silhouettes are common features used by many applications in computer vision. For many of these algorithms to perform optimally, accurately segmenting the objects of interest from the background to extract the silhouettes is essential. Motion segmentation is a popular technique to segment moving objects from the background, however such algorithms can be prone to poor segmentation, particularly in noisy or low contrast conditions. In this paper, the work of [3] combining motion detection with graph cuts, is extended into two novel implementations that aim to allow greater uncertainty in the output of the motion segmentation, providing a less restricted input to the graph cut algorithm. The proposed algorithms are evaluated on a portion of the ETISEO dataset using hand segmented ground truth data, and an improvement in performance over the motion segmentation alone and the baseline system of [3] is shown.

Group segmentation during object tracking using optical flow discontinuities

Within a surveillance video, occlusions are commonplace, and accurately resolving these occlusions is key when seeking to accurately track objects. The challenge of accurately segmenting objects is further complicated by the fact that within many real-world surveillance environments, the objects appear very similar. For example, footage of pedestrians in a city environment will consist of many people wearing dark suits. In this paper, we propose a novel technique to segment groups and resolve occlusions using optical flow discontinuities. We demonstrate that the ratio of continuous to discontinuous pixels within a region can be used to locate the overlapping edges, and incorporate this into an object tracking framework. Results on a portion of the ETISEO database show that the proposed algorithm results in improved tracking performance overall, and improved tracking within occlusions.

La théorie démocratique et La Physique Théorique

Le présent essai soutient, un peu le long d'une ligne simmelienne, que la théorie démocratique peut produire des théories pratiques et universelles, comme celles développées en physique théorique. Le raisonnement qui sous-tend cet essai est de montrer que la théorie de la «démocratie de base" peut-être vrai par le faite si on la comparer à la Relative Spécifique d’Einstein portant spécifiquement sur les paramètres de symétrie, l'unification, la simplicité et l'utilité. Ces paramètres sont ce qui fait qu’une théorie en physique comme ont la rencontre s’adapte non seulement aux connaissances actuelles, mais aussi de produire des chemins vers l'essai (application). Comme la théorie de la «démocratie de base » peut satisfaire ces mêmes paramètres, il pourrait trancher le débat relatif à la définition de la démocratie. Ceci sera d'abord soutenu pour discuter de ce qui est la théorie de la «démocratie de base» et pourquoi cela diffère des travaux précédents, en deuxième lieu, en expliquant les paramètres choisis (comme pour quoi ceux-ci et pas à d'autres confirment ou échouent les théories) et, troisièmement, en comparant comment la relativité et la théorie de la «démocratie de base » peut correspondre aux paramètres.

Segmentation of scenes of mobile objects and demonstrable backgrounds

In this paper we present a real-time foreground–background segmentation algorithm that exploits the following observation (very often satisfied by a static camera positioned high in its environment). If a blob moves on a pixel p that had not changed its colour significantly for a few frames, then p was probably part of the background when its colour was static. With this information we are able to update differentially pixels believed to be background. This work is relevant to autonomous minirobots, as they often navigate in buildings where smart surveillance cameras could communicate wirelessly with them. A by-product of the proposed system is a mask of the image regions which are demonstrably background. Statistically significant tests show that the proposed method has a better precision and recall rates than the state of the art foreground/background segmentation algorithm of the OpenCV computer vision library.

Bayes Factor based speaker segmentation for speaker diarization

This paper proposes the use of the Bayes Factor as a distance metric for speaker segmentation within a speaker diarization system. The proposed approach uses a pair of constant sized, sliding windows to compute the value of the Bayes Factor between the adjacent windows over the entire audio. Results obtained on the 2002 Rich Transcription Evaluation dataset show an improved segmentation performance compared to previous approaches reported in literature using the Generalized Likelihood Ratio. When applied in a speaker diarization system, this approach results in a 5.1% relative improvement in the overall Diarization Error Rate compared to the baseline.

Comparison of standard image segmentation methods for segmentation of brain tumors from 2D MR images

In the analysis of medical images for computer-aided diagnosis and therapy, segmentation is often required as a preliminary step. Medical image segmentation is a complex and challenging task due to the complex nature of the images. The brain has a particularly complicated structure and its precise segmentation is very important for detecting tumors, edema, and necrotic tissues in order to prescribe appropriate therapy. Magnetic Resonance Imaging is an important diagnostic imaging technique utilized for early detection of abnormal changes in tissues and organs. It possesses good contrast resolution for different tissues and is, thus, preferred over Computerized Tomography for brain study. Therefore, the majority of research in medical image segmentation concerns MR images. As the core juncture of this research a set of MR images have been segmented using standard image segmentation techniques to isolate a brain tumor from the other regions of the brain. Subsequently the resultant images from the different segmentation techniques were compared with each other and analyzed by professional radiologists to find the segmentation technique which is the most accurate. Experimental results show that the Otsu’s thresholding method is the most suitable image segmentation method to segment a brain tumor from a Magnetic Resonance Image.