Abstract: Pattern recognition and image recognition methods are commonly developed and tested using testbeds, which contain known responses to a query set. Until now, testbeds available for image analysis and content-based image retrieval (CBIR) have been scarce and small-scale. Here we present the one million images CEA-List Image Collection (CLIC) testbed that we have produced, and report on our use of this testbed to evaluate image analysis merging techniques. This testbed will soon be made publicly available through the EU MUSCLE Network of Excellence.
Abstract: In order to alleviate the mental and physical problems
of persons with disabilities, animal-assisted therapy (AAT) is one of
the possible modalities that employs the merit of the human-animal
interaction. Nevertheless, to achieve the purpose of AAT for persons
with severe disabilities (e.g. spinal cord injury, stroke, and
amyotrophic lateral sclerosis), real-time animal language
interpretation is desirable. Since canine behaviors can be visually
notable from its tail, this paper proposes the automatic real-time
interpretation of canine tail language for human-canine interaction in
the case of persons with severe disabilities. Canine tail language is
captured via two 3-axis accelerometers. Directions and frequencies
are selected as our features of interests. The novel fuzzy rules based
on Gaussian-Trapezoidal model and center of gravity (COG)-based
defuzzification method are proposed in order to interpret the features
into four canine emotional behaviors, i.e., agitate, happy, scare and
neutral as well as its blended emotional behaviors. The emotional
behavior model is performed in the simulated dog and has also been
evaluated in the real dog with the perfect recognition rate.
Abstract: Nowadays, hand vein recognition has attracted more attentions in identification biometrics systems. Generally, hand vein image is acquired with low contrast and irregular illumination. Accordingly, if you have a good preprocessing of hand vein image, we can easy extracted the feature extraction even with simple binarization. In this paper, a proposed approach is processed to improve the quality of hand vein image. First, a brief survey on existing methods of enhancement is investigated. Then a Radon Like features method is applied to preprocessing hand vein image. Finally, experiments results show that the proposed method give the better effective and reliable in improving hand vein images.
Abstract: In this paper we introduce a novel kernel classifier
based on a iterative shrinkage algorithm developed for compressive
sensing. We have adopted Bregman iteration with soft and hard
shrinkage functions and generalized hinge loss for solving l1 norm
minimization problem for classification. Our experimental results
with face recognition and digit classification using SVM as the
benchmark have shown that our method has a close error rate
compared to SVM but do not perform better than SVM. We have
found that the soft shrinkage method give more accuracy and in some
situations more sparseness than hard shrinkage methods.
Abstract: In this paper, a new proposed system for Persian
printed numeral characters recognition with emphasis on
representation and recognition stages is introduced. For the first time,
in Persian optical character recognition, geometrical central moments
as character image descriptor and fuzzy min-max neural network for
Persian numeral character recognition has been used. Set of different
experiments on binary images of regular, translated, rotated and
scaled Persian numeral characters has been done and variety of
results has been presented. The best result was 99.16% correct
recognition demonstrating geometrical central moments and fuzzy
min-max neural network are adequate for Persian printed numeral
character recognition.
Abstract: In this work, I present a review on Sparse Distributed
Memory for Small Cues (SDMSCue), a variant of Sparse Distributed
Memory (SDM) that is capable of handling small cues. I then conduct
and show some cognitive experiments on SDMSCue to test its
cognitive soundness compared to SDM. Small cues refer to input
cues that are presented to memory for reading associations; but have
many missing parts or fields from them. The original SDM failed to
handle such a problem. SDMSCue handles and overcomes this
pitfall. The main idea in SDMSCue; is the repeated projection of the
semantic space on smaller subspaces; that are selected based on the
input cue length and pattern. This process allows for Read/Write
operations using an input cue that is missing a large portion.
SDMSCue is augmented with the use of genetic algorithms for
memory allocation and initialization. I claim that SDM functionality
is a subset of SDMSCue functionality.
Abstract: This paper presents an effective method for detecting vehicles in front of the camera-assisted car during nighttime driving. The proposed method detects vehicles based on detecting vehicle headlights and taillights using techniques of image segmentation and clustering. First, to effectively extract spotlight of interest, a segmentation process based on automatic multi-level threshold method is applied on the road-scene images. Second, to spatial clustering vehicle of detecting lamps, a grouping process based on light tracking and locating vehicle lighting patterns. For simulation, we are implemented through Da-vinci 7437 DSP board with near infrared mono-camera and tested it in the urban and rural roads. Through the test, classification performances are above 97% of true positive rate evaluated on real-time environment. Our method also has good performance in the case of clear, fog and rain weather.
Abstract: Modern building automation needs to deal with very
different types of demands, depending on the use of a building and the
persons acting in it. To meet the requirements of situation awareness
in modern building automation, scenario recognition becomes more
and more important in order to detect sequences of events and to react
to them properly. We present two concepts of scenario recognition
and their implementation, one based on predefined templates and the
other applying an unsupervised learning algorithm using statistical
methods. Implemented applications will be described and their advantages
and disadvantages will be outlined.
Abstract: Support Vector Machine (SVM) is a statistical
learning tool that was initially developed by Vapnik in 1979 and later
developed to a more complex concept of structural risk minimization
(SRM). SVM is playing an increasing role in applications to
detection problems in various engineering problems, notably in
statistical signal processing, pattern recognition, image analysis, and
communication systems. In this paper, SVM was applied to the
detection of SAR (synthetic aperture radar) images in the presence of
partially developed speckle noise. The simulation was done for single
look and multi-look speckle models to give a complete overlook and
insight to the new proposed model of the SVM-based detector. The
structure of the SVM was derived and applied to real SAR images
and its performance in terms of the mean square error (MSE) metric
was calculated. We showed that the SVM-detected SAR images have
a very low MSE and are of good quality. The quality of the
processed speckled images improved for the multi-look model.
Furthermore, the contrast of the SVM detected images was higher
than that of the original non-noisy images, indicating that the SVM
approach increased the distance between the pixel reflectivity levels
(the detection hypotheses) in the original images.
Abstract: Detection and recognition of the Human Body Composition and extraction their measures (width and length of human body) in images are a major issue in detecting objects and the important field in Image, Signal and Vision Computing in recent years. Finding people and extraction their features in Images are particularly important problem of object recognition, because people can have high variability in the appearance. This variability may be due to the configuration of a person (e.g., standing vs. sitting vs. jogging), the pose (e.g. frontal vs. lateral view), clothing, and variations in illumination. In this study, first, Human Body is being recognized in image then the measures of Human Body extract from the image.
Abstract: In this paper, we propose a novel fast search algorithm for short MPEG video clips from video database. This algorithm is based on the adjacent pixel intensity difference quantization (APIDQ) algorithm, which had been reliably applied to human face recognition previously. An APIDQ histogram is utilized as the feature vector of the frame image. Instead of fully decompressed video frames, partially decoded data, namely DC images are utilized. Combined with active search [4], a temporal pruning algorithm, fast and robust video search can be realized. The proposed search algorithm has been evaluated by 6 hours of video to search for given 200 MPEG video clips which each length is 15 seconds. Experimental results show the proposed algorithm can detect the similar video clip in merely 80ms, and Equal Error Rate (ERR) of 3 % is achieved, which is more accurately and robust than conventional fast video search algorithm.
Abstract: This paper discusses the cued speech recognition
methods in videoconference. Cued speech is a specific gesture
language that is used for communication between deaf people. We
define the criteria for sentence intelligibility according to answers of
testing subjects (deaf people). In our tests we use 30 sample videos
coded by H.264 codec with various bit-rates and various speed of
cued speech. Additionally, we define the criteria for consonant sign
recognizability in single-handed finger alphabet (dactyl) analogically
to acoustics. We use another 12 sample videos coded by H.264 codec
with various bit-rates in four different video formats. To interpret the
results we apply the standard scale for subjective video quality
evaluation and the percentual evaluation of intelligibility as in
acoustics. From the results we construct the minimum coded bit-rate
recommendations for every spatial resolution.
Abstract: Automatic determination of blood in less bright or
noisy capsule endoscopic images is difficult due to low S/N ratio.
Especially it may not be accurate to analyze these images due to the
influence of external disturbance. Therefore, we proposed detection
methods that are not dependent only on color bands. In locating
bleeding regions, the identification of object outlines in the frame and
features of their local colors were taken into consideration. The results
showed that the capability of detecting bleeding was much improved.
Abstract: Today, Higher Education in a global scope is subordinated to the greater institutional controls through the policies of the Quality of Education. These include processes of over evaluation of all the academic activities: students- and professors- performance, educational logistics, managerial standards for the administration of institutions of higher education, as well as the establishment of the imaginaries of excellence and prestige as the foundations on which universities of the XXI century will focus their present and future goals and interests. But at the same time higher education systems worldwide are facing the most profound crisis of sense and meaning and attending enormous mutations in their identity. Based in a qualitative research approach, this paper shows the social configurations that the scholars at the Universities in Mexico build around the discourse of the Quality of Education, and how these policies put in risk the social recognition of these individuals.
Abstract: Classifier fusion may generate more accurate
classification than each of the basic classifiers. Fusion is often based
on fixed combination rules like the product, average etc. This paper
presents decision templates as classifier fusion method for the
recognition of the handwritten English and Farsi numerals (1-9).
The process involves extracting a feature vector on well-known
image databases. The extracted feature vector is fed to multiple
classifier fusion. A set of experiments were conducted to compare
decision templates (DTs) with some combination rules. Results from
decision templates conclude 97.99% and 97.28% for Farsi and
English handwritten digits.
Abstract: The task of face recognition has been actively
researched in recent years. This paper provides an up-to-date review of major human face recognition research. We first present an
overview of face recognition and its applications. Then, a literature review of the most recent face recognition techniques is presented.
Description and limitations of face databases which are used to test
the performance of these face recognition algorithms are given. A
brief summary of the face recognition vendor test (FRVT) 2002, a
large scale evaluation of automatic face recognition technology, and
its conclusions are also given. Finally, we give a summary of the research results.
Abstract: The standard investigational method for obstructive
sleep apnea syndrome (OSAS) diagnosis is polysomnography (PSG),
which consists of a simultaneous, usually overnight recording of
multiple electro-physiological signals related to sleep and
wakefulness. This is an expensive, encumbering and not a readily
repeated protocol, and therefore there is need for simpler and easily
implemented screening and detection techniques. Identification of
apnea/hypopnea events in the screening recordings is the key factor
for the diagnosis of OSAS. The analysis of a solely single-lead
electrocardiographic (ECG) signal for OSAS diagnosis, which may
be done with portable devices, at patient-s home, is the challenge of
the last years. A novel artificial neural network (ANN) based
approach for feature extraction and automatic identification of
respiratory events in ECG signals is presented in this paper. A
nonlinear principal component analysis (NLPCA) method was
considered for feature extraction and support vector machine for
classification/recognition. An alternative representation of the
respiratory events by means of Kohonen type neural network is
discussed. Our prospective study was based on OSAS patients of the
Clinical Hospital of Pneumology from Iaşi, Romania, males and
females, as well as on non-OSAS investigated human subjects. Our
computed analysis includes a learning phase based on cross signal
PSG annotation.
Abstract: Face recognition is a technique to automatically
identify or verify individuals. It receives great attention in
identification, authentication, security and many more applications.
Diverse methods had been proposed for this purpose and also a lot of
comparative studies were performed. However, researchers could not
reach unified conclusion. In this paper, we are reporting an extensive
quantitative accuracy analysis of four most widely used face
recognition algorithms: Principal Component Analysis (PCA),
Independent Component Analysis (ICA), Linear Discriminant
Analysis (LDA) and Support Vector Machine (SVM) using AT&T,
Sheffield and Bangladeshi people face databases under diverse
situations such as illumination, alignment and pose variations.
Abstract: A new approach for facial expressions recognition based on face context and adaptively weighted sub-pattern PCA (Aw-SpPCA) has been presented in this paper. The facial region and others part of the body have been segmented from the complex environment based on skin color model. An algorithm has been proposed to accurate detection of face region from the segmented image based on constant ratio of height and width of face (δ= 1.618). The paper also discusses on new concept to detect the eye and mouth position. The desired part of the face has been cropped to analysis the expression of a person. Unlike PCA based on a whole image pattern, Aw-SpPCA operates directly on its sub patterns partitioned from an original whole pattern and separately extracts features from them. Aw-SpPCA can adaptively compute the contributions of each part and a classification task in order to enhance the robustness to both expression and illumination variations. Experiments on single standard face with five types of facial expression database shows that the proposed method is competitive.
Abstract: Human activity is a major concern in a wide variety of
applications, such as video surveillance, human computer interface
and face image database management. Detecting and recognizing
faces is a crucial step in these applications. Furthermore, major
advancements and initiatives in security applications in the past years
have propelled face recognition technology into the spotlight. The
performance of existing face recognition systems declines significantly
if the resolution of the face image falls below a certain level.
This is especially critical in surveillance imagery where often, due to
many reasons, only low-resolution video of faces is available. If these
low-resolution images are passed to a face recognition system, the
performance is usually unacceptable. Hence, resolution plays a key
role in face recognition systems. In this paper we introduce a new
low resolution face recognition system based on mixture of expert
neural networks. In order to produce the low resolution input images
we down-sampled the 48 × 48 ORL images to 12 × 12 ones using
the nearest neighbor interpolation method and after that applying
the bicubic interpolation method yields enhanced images which is
given to the Principal Component Analysis feature extractor system.
Comparison with some of the most related methods indicates that
the proposed novel model yields excellent recognition rate in low
resolution face recognition that is the recognition rate of 100% for
the training set and 96.5% for the test set.