Iet Computer Vision 

About: Iet Computer Vision is an academic journal published by Institution of Engineering and Technology. The journal publishes majorly in the area(s): Feature extraction & Computer science. It has an ISSN identifier of 1751-9632. It is also open access. Over the lifetime, 1047 publications have been published receiving 12522 citations. The journal is also known as: Institution of Engineering and Technology computer vision & Computer vision, IET.

Robust mean-shift tracking with corrected background-weighted histogram

Abstract: The background-weighted histogram (BWH) algorithm proposed by Comaniciu et al. attempts to reduce the interference of background in target localisation in mean-shift tracking. However, the authors prove that the weights assigned to pixels in the target candidate region by BWH are proportional to those without background information, that is, BWH does not introduce any new information because the mean-shift iteration formula is invariant to the scale transformation of weights. Then a corrected BWH (CBWH) formula is proposed by transforming only the target model but not the target candidate model. The CBWH scheme can effectively reduce background's interference in target localisation. The experimental results show that CBWH can lead to faster convergence and more accurate localisation than the usual target representation in mean-shift tracking. Even if the target is not well initialised, the proposed algorithm can still robustly track the object, which is hard to achieve by the conventional target representation.

Infrared imaging of hand vein patterns for biometric purposes

Abstract: A novel non-invasive imaging technique to image the vein patterns in various parts of the hand for biometric purposes is evaluated. Two imaging methods are investigated: far-infrared (FIR) thermography and near-infrared (NIR) imaging. Experiments involving data acquisition from various parts of the hand, including the back of the hand, palm and wrist, were carried out using both imaging techniques. Analysis of the data collected shows that FIR thermography is less successful at capturing veins in the palm and wrist. FIR thermography can capture the large veins in the back of the hand, but it is sensitive to ambient temperature and humidity conditions as well as human body temperature. NIR imaging produces good quality images when capturing veins in the back of the hand, palm and wrist. NIR imaging is also more tolerant to changes in the environment and body condition but faces the problem of pattern corruption because of visible skin features being mistaken for veins. This corruption is not present in FIR imaging. An initial biometric system is investigated to test both FIR and NIR images for biometric purposes. The results show all the subjects were correctly identified, which indicates vein pattern biometrics with infrared imaging is a potentially useful biometric.

Brain tumour classification using two-tier classifier with adaptive segmentation technique

Abstract: A brain tumour is a mass of tissue that is structured by a gradual addition of anomalous cells and it is important to classify brain tumours from the magnetic resonance imaging (MRI) for treatment. Human investigation is the routine technique for brain MRI tumour detection and tumours classification. Interpretation of images is based on organised and explicit classification of brain MRI and also various techniques have been proposed. Information identified with anatomical structures and potential abnormal tissues which are noteworthy to treat are given by brain tumour segmentation on MRI, the proposed system uses the adaptive pillar K-means algorithm for successful segmentation and the classification methodology is done by the two-tier classification approach. In the proposed system, at first the self-organising map neural network trains the features extracted from the discrete wavelet transform blend wavelets and the resultant filter factors are consequently trained by the K-nearest neighbour and the testing process is also accomplished in two stages. The proposed two-tier classification system classifies the brain tumours in double training process which gives preferable performance over the traditional classification method. The proposed system has been validated with the support of real data sets and the experimental results showed enhanced performance.

Band selection for hyperspectral imagery using affinity propagation (Selected papers from the Digital Image Computing: Techniques and Applications Conference 2008 (DICTA 2008))

Abstract: Hyperspectral imagery generally contains enormous amounts of data because of hundreds of spectral bands. Band selection is often adopted to reduce computational cost and accelerate knowledge discovery and other tasks such as subsequent classification. An exemplar-based clustering algorithm termed affinity propagation for band selection is proposed. Affinity propagation is derived from factor graph, and operates by initially considering all data points as potential cluster centres (exemplars) and then exchanging messages between data points until a good set of exemplars and clusters emerges. Affinity propagation has been applied to computer vision and bioinformatics, and shown to be much faster than other clustering methods for large data. By combining the information about the discriminative capability of each individual band and the correlation/similarity between bands, the exemplars generated by affine propagation have higher importance and less correlation/similarity. The performance of band selection is evaluated through a pixel image classification task. Experimental results demonstrate that, compared with some popular band selection methods, the bands selected by affinity propagation best characterise the hyperspectral imagery from the pixel classification standpoint.

Face recognition: challenges, achievements and future directions

Abstract: Face recognition has received significant attention because of its numerous applications in access control, law enforcement, security, surveillance, Internet communication and computer entertainment. Although significant progress has been made, the state-of-the-art face recognition systems yield satisfactory performance only under controlled scenarios and they degrade significantly when confronted with real-world scenarios. The real-world scenarios have unconstrained conditions such as illumination and pose variations, occlusion and expressions. Thus, there remain plenty of challenges and opportunities ahead. Latterly, some researchers have begun to examine face recognition under unconstrained conditions. Instead of providing a detailed experimental evaluation, which has been already presented in the referenced works, this study serves more as a guide for readers. Thus, the goal of this study is to discuss the significant challenges involved in the adaptation of existing face recognition algorithms to build successful systems that can be employed in the real world. Then, it discusses what has been achieved so far, focusing specifically on the most successful algorithms, and overviews the successes and failures of these algorithms to the subject. It also proposes several possible future directions for face recognition. Thus, it will be a good starting point for research projects on face recognition as useful techniques can be isolated and past errors can be avoided.