23 research outputs found
Pattern Recognition
Pattern recognition is a very wide research field. It involves factors as diverse as sensors, feature extraction, pattern classification, decision fusion, applications and others. The signals processed are commonly one, two or three dimensional, the processing is done in real- time or takes hours and days, some systems look for one narrow object class, others search huge databases for entries with at least a small amount of similarity. No single person can claim expertise across the whole field, which develops rapidly, updates its paradigms and comprehends several philosophical approaches. This book reflects this diversity by presenting a selection of recent developments within the area of pattern recognition and related fields. It covers theoretical advances in classification and feature extraction as well as application-oriented works. Authors of these 25 works present and advocate recent achievements of their research related to the field of pattern recognition
State of the Art in Face Recognition
Notwithstanding the tremendous effort to solve the face recognition problem, it is not possible yet to design a face recognition system with a potential close to human performance. New computer vision and pattern recognition approaches need to be investigated. Even new knowledge and perspectives from different fields like, psychology and neuroscience must be incorporated into the current field of face recognition to design a robust face recognition system. Indeed, many more efforts are required to end up with a human like face recognition system. This book tries to make an effort to reduce the gap between the previous face recognition research state and the future state
Structured representation learning from complex data
This thesis advances several theoretical and practical aspects of the recently introduced restricted Boltzmann machine - a powerful probabilistic and generative framework for modelling data and learning representations. The contributions of this study represent a systematic and common theme in learning structured representations from complex data
Computational intelligence approaches to robotics, automation, and control [Volume guest editors]
No abstract available
A survey on video compression fast block matching algorithms
Video compression is the process of reducing the amount of data required to represent digital video while preserving an acceptable video quality. Recent studies on video compression have focused on multimedia transmission, videophones, teleconferencing, high definition television, CD-ROM storage, etc. The idea of compression techniques is to remove the redundant information that exists in the video sequences.
Motion compensation predictive coding is the main coding tool for removing temporal redundancy of video sequences and it typically accounts for 50–80% of video encoding complexity. This technique has been adopted by all of the existing International Video Coding Standards. It assumes that the current frame can be locally modelled as a translation of the reference frames. The practical and widely method used to carry out motion compensated prediction is block matching algorithm. In this method, video frames are divided into a set of non-overlapped macroblocks and compared with the search area in the reference frame in order to find the best matching macroblock. This will carry out displacement vectors that stipulate the movement of the macroblocks from one location to another in the reference frame. Checking all these locations is called Full Search, which provides the best result. However, this algorithm suffers from long computational time, which necessitates improvement. Several methods of Fast Block Matching algorithm are developed to reduce the computation complexity.
This paper focuses on a survey for two video compression techniques: the first is called the lossless block matching algorithm process, in which the computational time required to determine the matching macroblock of the Full Search is decreased while the resolution of the predicted frames is the same as for the Full Search. The second is called lossy block matching algorithm process, which reduces the computational complexity effectively but the search result's quality is not the same as for the Full Search
Image Classification of High Variant Objects in Fast Industrial Applications
Recent advances in machine learning and image processing have expanded the applications of computer vision
in many industries. In industrial applications, image classification is a crucial task since high variant objects
present difficult problems because of their variety and constant change in attributes. Computer vision algorithms
can function effectively in complex environments, working alongside human operators to enhance efficiency and
data accuracy. However, there are still many industries facing difficulties with automation that have not yet been
properly solved and put into practice. They have the need for more accurate, convenient, and faster methods.
These solutions drove my interest in combining multiple learning strategies as well as sensors and image formats
to enable the use of computer vision for these applications. The motivation for this work is to answer a number of
research questions that aim to mitigate current problems in hinder their practical application. This work therefore
aims to present solutions that contribute to enabling these solutions. I demonstrate why standard methods cannot
simply be applied to an existing problem. Each method must be customized to the specific application scenario
in order to obtain a working solution.
One example is face recognition where the classification performance is crucial for the system’s ability to
correctly identify individuals. Additional features would allow higher accuracy, robustness, safety, and make
presentation attacks more difficult. The detection of attempted attacks is critical for the acceptance of such
systems and significantly impacts the applicability of biometrics. Another application is tailgating detection
at automated entrance gates. Especially in high security environments it is important to prevent that authorized
persons can take an unauthorized person into the secured area. There is a plethora of technology that seem potentially
suitable but there are several practical factors to consider that increase or decrease applicability depending
which method is used. The third application covered in this thesis is the classification of textiles when they are
not spread out. Finding certain properties on them is complex, as these properties might be inside a fold, or differ
in appearance because of shadows and position.
The first part of this work provides in-depth analysis of the three individual applications, including background
information that is needed to understand the research topic and its proposed solutions. It includes the state of
the art in the area for all researched applications. In the second part of this work, methods are presented to
facilitate or enable the industrial applicability of the presented applications. New image databases are initially
presented for all three application areas. In the case of biometrics, three methods that identify and improve
specific performance parameters are shown. It will be shown how melanin face pigmentation (MFP) features
can be extracted and used for classification in face recognition and PAD applications. In the entrance control
application, the focus is on the sensor information with six methods being presented in detail. This includes the
use of thermal images to detect humans based on their body heat, depth images in form of RGB-D images and
2D image series, as well as data of a floor mounted sensor-grid. For textile defect detection several methods and
a novel classification procedure, in free-fall is presented.
In summary, this work examines computer vision applications for their practical industrial applicability and
presents solutions to mitigate the identified problems. In contrast to previous work, the proposed approaches are
(a) effective in improving classification performance (b) fast in execution and (c) easily integrated into existing
processes and equipment