Vehicle detection, counting and finally classification has been an important aspect of traffic analysis specially on highways in many developed and developing nations. This has vitalized the monitoring of freeways and reduced the reliance on human traffic monitors specially in developed nations. However, much research carried out in this regard since 1995 have been slow to progress until around 2000. Since then, much more encouraging outcomes have been achieved. This has been mainly due to the advances on vision-based computing and the miniaturizing of hardware since the early 2000. Initial vision-based systems used basic computer vision approaches such as background subtraction and edge detection to detect and count vehicles. Early progress of classification had little success until around 2010. Since 2010, many computer vision approaches using Neural Networks have been gradually increasing the efficiency and the realtime operation of such systems. Since then, many deep learning based neural network approaches have produced remarkable outcomes specially in classification. We have also reported highly accurate and efficient deep learning based YOLOv5 system that has recorded an astounding success. We have used models YOLOv5l, YOLOv5m, YOLOv5n and YOLOv5s in our research to ascertain the best model for the task. However, low light conditions associated with overcast, and dusk have significantly reduced the accuracy in many deep learning-based systems. There are other techniques based on approaches such as headlight detection have appeared to increase the accuracy of counting however, classification at night without adequate lighting still pauses a formidable challenge. The work presented here analyses the vehicle counting problem using computer vision providing many different viewpoints in a view to provide the reader with accurate problem definition.

Thinking Head project is a multidisciplinary approach to building intelligent agents for human machine interaction. The Thinking Head Framework evolved out of the Thinking Head Project and it facilitates loose coupling between various components and forms the central nerve system in a multimodal perception-action system. The paper presents the overall architecture, components and the attention system. The paper then concludes with a preliminary behavioral experiment that studies the intelligibility of the audiovisual speech output produced by the Embodied Conversational Agent (ECA) that is part of the system. These results provide the baseline for future evaluations of the system as the project progresses through multiple evaluate and refine cycles.

This paper presents a novel approach for sketch-based image retrieval based on low-level features. It enables the measuring of the similarity among full color multi-component images within a database (models) and simple black and white user sketched queries. It needs no cost intensive image segmentation. Strong edges of the model image and morphologically thinned version of the query image are used for image abstraction. Angular-radial decomposition of pixels in the abstract images is used to extract new compact and affine invariant features. Comparative results, employing an art database (ArT BANK), show significant improvement in average normalized modified retrieval rank (ANMRR) using the proposed features.

This work presents a novel fast method for line segment extraction, based on a chain code representation of edge maps. It has a parallel nature and can be employed on parallel machines. In the first phase it breaks the macro chains into several micro chains after applying shifting, smoothing and differentiating. The micro chains are then approximated by straight line segments. In the second phase, based on the length and the error criteria, the line segments are grouped into much longer lines. The experimental results show a significant improvement in the number of line segments extracted while their accumulative length keeps high.

A new approach for document image analysis and verification is presented. The approach utilizes connected component analysis and geometric properties of labelled regions for region of interest extraction. Document images containing Persian/Arabic text combined with English text, headlines, ruling lines, trade mark and cursive signature are used as a test data. Persian/Arabic signature extraction is investigated as a case study. The proposed method uses special characteristics of such signatures for extraction and verification procedures. A set of efficient, invariant and compact features is extracted utilizing spatial partitioning of the signature region. Comparative results exhibit high extraction and verification rates