Mobile Platform Challenges in Interactive Computer Vision

Computer vision can be used to increase the interactivity of existing and new camera-based applications. It can be used to build novel interaction methods and user interfaces. The computing and sensing needs of this kind of applications require a careful balance between quality and performance, a practical trade-off. This chapter shows the importance of using all the available resources to hide application latency and maximize computational throughput. The experience gained during the developing of interactive applications is utilized to characterize the constraints imposed by the mobile environment, discussing the most important design goals: high performance and low power consumption. In addition, this chapter discusses the use of heterogeneous computing via asymmetric multiprocessing to improve the throughput and energy efficiency of interactive vision-based applications.

Parallel Computing in Face Image Retrieval

Modern digital photo collections contain vast multitudes of high-resolution color images, many containing faces, which are desirable to retrieve visually. This poses a problem for effective image browsing and calls for efficient Content Based Image Retrieval (CBIR) capabilities ensuring near-instantaneous visual query turn-around. This in turn necessitates parallelization of many existing image processing and information retrieval algorithms that can no longer satisfy the modern user demands, when executed sequentially. Hence a practical approach to Face Image Retrieval (FIR) is presented. It utilizes multi-core processing architectures to implement its major modules (e.g. face detection and matching) efficiently without sacrificing the image retrieval accuracy. The integration of FIR into a web-based family reunification system demonstrates the practicality of the proposed method. Several accuracy and speed evaluations on real-word data are presented and possible CBIR extensions are discussed.

Computer Vision Based Classification on Commercial Videos

Computer vision is a study which is concerned with automatic mining, analysis, perception, and extraction of the essential information from a single frame or image and a sequence of frames. It focuses on the development of automatic visual perception systems to reconstruct and interpret a three-dimensional scene from two-dimensional images through the properties of the structures in the scene. This is a challenging task for the contemporary computer vision system. Hence, this chapter explores the essential information, processing, analysis, and understanding necessary for computer vision. This enables users to retrieve product-based advertisement content and efficient browsing of desired shows. The final goal of this chapter is to design electronic embedded systems focused on technology integration with a domestic utility concept.

A Technical Assessment on License Plate Detection System

Identifying the region of interest from an image is an important task in the field of computer vision. This process is referred as Object Detection. Locating the object can be done by extracting the features from the image and the features depend on the application. Image retrieval and surveillance are two important applications of Object Detection. Surveillance is an active research topic in computer vision that tries to detect, recognize and track objects over images. An interesting application of it is License Plate Recognition module in Intelligent Transportation System. The speedy developments in economic and social life bring on for a large increase in the number of vehicles in the city. This makes the traffic management difficult. It has great impact on human life as it aims to increase the transportation safety through innovative technologies. An important function that need for majority of ITS application is to identify the vehicle using LPR. This chapter presents assessment on different methods in detecting the license plate and discusses a case study on it.

FPGA-Based Re-Configurable Architecture for Window-Based Image Processing

In this proposed book chapter, a simple but efficient presentation of Median Filter, Switching Median Filter, Adaptive Median Filter and Decision-Based Adaptive Filtering Method and their hardware architecture for FPGA is described for removal of up to 99% impulse noise from Digital Images. For hardware architecture, simulation is done using Xilinx ISE 14.5 software of XILINX. For implementation, these approaches utilize Genesys VIRTEX V FPGA device of XC5VLX50T device family. In this approach, we proposed an efficient design for suppression of impulse noise from digital images corrupted by up to 99% impulse noise using decision based adaptive filtering method as well as preserve the details of image. The method works in two different stages – noise detection using switching technique and finally noise suppression and restoration. Experimental results show that our method perform better in terms of PSNR below 80% noise density but above 80% noise density it is almost comparable with the latest methods.

Creating Sound Glyph Database for Video Subtitling

Accessibility of speech information in videos is a huge challenge for the hearing impaired, making a visual representation such as text subtitling essential. Unavailability of a good Automatic Speech Recognition (ASR) engine, makes automatic generation of text subtitles for resource deficient languages such as Indian languages, extremely difficult. Techniques to build such an ASR using audio and corresponding transcription in the form of broadcast news or audio books have been proposed; however, these techniques require transcriptions corresponding to the audio in editable text format, which are unavailable for resource deficient languages. In this chapter, a novel technique of building a sound-glyph database for a resource deficient language has been described. The sound-glyph database can be used effectively to subtitle videos in the same language script. Considering large volumes of data that need to be processed, we propose a parallel processing method in a multiresolution setup, harnessing the multi-core capacity of present day computers.

Fish Tracking with Computer Vision Techniques

Vertical slot fishways are hydraulic structures which allow the upstream migration of fish through obstructions in rivers. Their design depends on the interplay between hydraulic and biological variables to match the requirements of the fish species for which they are intended. However, current mechanisms to study fish behavior in fishway models are impractical or unduly affect the animal behavior. In this chapter, we propose a new procedure for measuring fish behavior in fishways using Computer Vision (CV) techniques to analyze images obtained from the assays by means of a camera system designed for fishway integration. It is expected that this technique will provide detailed information about the fish behavior and will help to improve fish passage devices. A series of assays have been performed in order to validate this new approach in a full-scale fishway model and with living fishes. We have obtained very promising results that allow reconstructing correctly the movements of the fish within the fishway without disturbing fish.

Controlling Prosthetic Limb Movements Using EEG Signals

This chapter focuses on replacing natural arms with artificial arms with movement controlled by EEG signals. The selected features were classified using C4.5 decision tree algorithm, best first decision tree algorithm, Naïve Bayes algorithm, Bayes net algorithm, K star algorithm and ripple down rule learner algorithm. The results of statistical and histogram features are discussed and conclusions of the study are presented.

Fish Monitoring, Sizing, and Detection Using Stereovision, Laser Technology, and Computer Vision

Stereovision and laser techniques allow for getting knowledge about fish, mostly when they are combined with computer vision. This kind of techniques avoid to use traditional procedures such as direct observation, which are impractical or can affect the fish behavior, in task such as aquarium and fish farm management or fishway, like vertical slot fishway, evaluation. This chapter describes in a first stage, the use stereovision join with computer vision to fish monitoring and measure size of fishes. In the second part, using laser technology and computer vision to fish detection, especially in slot fishways. Vertical slot fishways are structures that are placed in rivers to allow fish to avoid obstacles such as dams, hydroelectric plants. Then, it shows a results section and finally authors' conclusions.