List of Pattern Recognition IEEE 2006 Projects. It Contains the IEEE Projects in the Domain Pattern Recognition for M.E Computer Science students.

1. M.E Computer Science Pattern Recognition Projects
Web : www.kasanpro.com Email : sales@kasanpro.com
List Link : http://kasanpro.com/projects-list/m-e-computer-science-pattern-recognition-projects
Title :Features selection of cotton disease leaves image based on fuzzy feature selection techniques
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/cotton-disease-fuzzy-feature-selection-detection
Abstract : In the research of identifying and diagnosing cotton disease using computer vision intellectively in the
agriculture, feature selection is a key question in pattern recognition and affects the design and performance of the
classifier. In this paper, the fuzzy feature selection approach -fuzzy curves (FC) and surfaces (FS) - is proposed to
select features of cotton disease leaves image. In order to get best information for diagnosing and identifying, a
subset of independent significant features is identified exploiting the fuzzy feature selection approach. Firstly, utilize
FC to automatically and quickly isolate a small set of significant features from the set of original features according to
their significance and eliminate spurious features; then, use FS to get rid of the features dependent on the significant
features. This approach reduces the dimensionality of the feature space so that lead to a simplified classification
scheme appropriate for practical classification applications. The results show that the effectiveness of features
selected by the FC and FS method is much better than that selected by human randomly or other methods.
Title :IntentSearch: Capturing User Intention for One-Click Internet Image Search
Language : ASP.NET with C#
Project Link :
http://kasanpro.com/p/asp-net-with-c-sharp/intentsearch-capturing-user-intention-one-click-internet-image-search
Abstract : Web-scale image search engines (e.g., Google image search, Bing image search) mostly rely on
surrounding text features. It is difficult for them to interpret users' search intention only by query keywords and this
leads to ambiguous and noisy search results which are far from satisfactory. It is important to use visual information in
order to solve the ambiguity in text-based image retrieval. In this paper, we propose a novel Internet image search
approach. It only requires the user to click on one query image with minimum effort and images from a pool retrieved
by text-based search are reranked based on both visual and textual content. Our key contribution is to capture the
users' search intention from this one-click query image in four steps. 1. The query image is categorized into one of the
predefined adaptive weight categories which reflect users' search intention at a coarse level. Inside each category, a
specific weight schema is used to combine visual features adaptive to this kind of image to better rerank the
text-based search result. 2. Based on the visual content of the query image selected by the user and through image
clustering, query keywords are expanded to capture user intention. 3. Expanded keywords are used to enlarge the
image pool to contain more relevant images. 4. Expanded keywords are also used to expand the query image to
multiple positive visual examples from which new query specific visual and textual similarity metrics are learned to
further improve content-based image reranking. All these steps are automatic, without extra effort from the user. This
is critically important for any commercial web-based image search engine, where the user interface has to be
extremely simple. Besides this key contribution, a set of visual features which are both effective and efficient in
Internet image search are designed. Experimental evaluation shows that our approach significantly improves the
precision of top ranked images and also the user experience.
Title :IntentSearch: Capturing User Intention for One-Click Internet Image Search
Language : ASP.NET with VB
Project Link :
http://kasanpro.com/p/asp-net-with-vb/intentsearch-capturing-user-intention-one-click-internet-image-search-code
Abstract : Web-scale image search engines (e.g., Google image search, Bing image search) mostly rely on
surrounding text features. It is difficult for them to interpret users' search intention only by query keywords and this
leads to ambiguous and noisy search results which are far from satisfactory. It is important to use visual information in
order to solve the ambiguity in text-based image retrieval. In this paper, we propose a novel Internet image search
approach. It only requires the user to click on one query image with minimum effort and images from a pool retrieved

2. by text-based search are reranked based on both visual and textual content. Our key contribution is to capture the
users' search intention from this one-click query image in four steps. 1. The query image is categorized into one of the
predefined adaptive weight categories which reflect users' search intention at a coarse level. Inside each category, a
specific weight schema is used to combine visual features adaptive to this kind of image to better rerank the
text-based search result. 2. Based on the visual content of the query image selected by the user and through image
clustering, query keywords are expanded to capture user intention. 3. Expanded keywords are used to enlarge the
image pool to contain more relevant images. 4. Expanded keywords are also used to expand the query image to
multiple positive visual examples from which new query specific visual and textual similarity metrics are learned to
further improve content-based image reranking. All these steps are automatic, without extra effort from the user. This
is critically important for any commercial web-based image search engine, where the user interface has to be
extremely simple. Besides this key contribution, a set of visual features which are both effective and efficient in
Internet image search are designed. Experimental evaluation shows that our approach significantly improves the
precision of top ranked images and also the user experience.
Title :IntentSearch: Capturing User Intention for One-Click Internet Image Search
Language : PHP
Project Link :
http://kasanpro.com/p/php/intentsearch-capturing-user-intention-one-click-internet-image-search-implement
Abstract : Web-scale image search engines (e.g., Google image search, Bing image search) mostly rely on
surrounding text features. It is difficult for them to interpret users' search intention only by query keywords and this
leads to ambiguous and noisy search results which are far from satisfactory. It is important to use visual information in
order to solve the ambiguity in text-based image retrieval. In this paper, we propose a novel Internet image search
approach. It only requires the user to click on one query image with minimum effort and images from a pool retrieved
by text-based search are reranked based on both visual and textual content. Our key contribution is to capture the
users' search intention from this one-click query image in four steps. 1. The query image is categorized into one of the
predefined adaptive weight categories which reflect users' search intention at a coarse level. Inside each category, a
specific weight schema is used to combine visual features adaptive to this kind of image to better rerank the
text-based search result. 2. Based on the visual content of the query image selected by the user and through image
clustering, query keywords are expanded to capture user intention. 3. Expanded keywords are used to enlarge the
image pool to contain more relevant images. 4. Expanded keywords are also used to expand the query image to
multiple positive visual examples from which new query specific visual and textual similarity metrics are learned to
further improve content-based image reranking. All these steps are automatic, without extra effort from the user. This
is critically important for any commercial web-based image search engine, where the user interface has to be
extremely simple. Besides this key contribution, a set of visual features which are both effective and efficient in
Internet image search are designed. Experimental evaluation shows that our approach significantly improves the
precision of top ranked images and also the user experience.
Title :Indian License Plate Character Recognition System using Neural Network
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/indian-license-plate-character-recognition-system-neural-network
Abstract : Intelligent Transportation System (ITS) has become an integral part of the Transportation Industry these
days and it consists of License Plate Recognition (LPR) System. License Plate Recognition is also called Car Plate
Recognition (CPR) or Automatic Number Plate Recognition (ANPR) System. In LPR System, when a vehicle steps
over magnetic loop detector it senses car and takes image of the car, following image preprocessing operations for
improvement in the quality of car image. From this enhanced image, license plate region is recognized and extracted.
Then character fragmentation/segmentation is performed on extracted License Plate and these segmented characters
are recognized using Neural Network in this paper.
M.E Computer Science Pattern Recognition Projects
Title :Hilbert-Huang transform based physiological signals analysis for emotion recognition
Language : Matlab
Project Link :
http://kasanpro.com/p/matlab/hilbert-huang-transform-based-physiological-signals-analysis-emotion-recognition
Abstract : This paper presents a feature extraction technique based on the Hilbert-Huang Transform (HHT) method
for emotion recognition from physiological signals. Four kinds of physiological signals were used for analysis:
electrocardiogram (ECG), electromyogram (EMG), skin conductivity (SC) and respiration changes (RSP). Each signal
is decomposed into a finite set of AM-FM mono components (fission process) by the Empirical Mode Decomposition

3. (EMD) which is the key part of the HHT. The information components of interest are then combined to create feature
vectors (fusion process) for the next classification stage. In addition, classification is performed by using Support
Vector Machines (SVM). The classification scores show that HHT based methods outperform traditional statistical
techniques and provide a promising framework for both analysis and recognition of physiological signals in emotion
recognition.
Title :Hilbert-Huang transform based physiological signals analysis for emotion recognition
Language : C#
Project Link :
http://kasanpro.com/p/c-sharp/hilbert-huang-transform-based-physiological-signals-analysis-emotion-recognition-code
Abstract : This paper presents a feature extraction technique based on the Hilbert-Huang Transform (HHT) method
for emotion recognition from physiological signals. Four kinds of physiological signals were used for analysis:
electrocardiogram (ECG), electromyogram (EMG), skin conductivity (SC) and respiration changes (RSP). Each signal
is decomposed into a finite set of AM-FM mono components (fission process) by the Empirical Mode Decomposition
(EMD) which is the key part of the HHT. The information components of interest are then combined to create feature
vectors (fusion process) for the next classification stage. In addition, classification is performed by using Support
Vector Machines (SVM). The classification scores show that HHT based methods outperform traditional statistical
techniques and provide a promising framework for both analysis and recognition of physiological signals in emotion
recognition.
Title :Automated ECG diagnostic P-wave analysis using wavelets
Language : Matlab
Project Link : http://kasanpro.com/p/matlab/automated-ecg-diagnostic-p-wave-analysis-wavelets
Abstract : P-wave characteristics in the human ECG are an important source of information in the diagnosis of atrial
conduction pathology. However, diagnosis by visual inspection is a difficult task since the P-wave is relatively small
and noise masking is often present. This paper introduces novel wavelet characteristics derived from the continuous
wavelet trans- form (CWT) which are shown to be potentially effective discriminators in an automated diagnostic
process. Characteristics of the 12-lead ECG P-wave were derived using CWT and statistical methods. A normal
control group and an abnormal (atrial conduction pathology) group were compared. The wavelet characteristics
captured frequency, magnitude and variance components of the P-wave. The best individual characteristics (i.e. ones
that significantly discriminated the groups) were entered into a linear discriminant analysis (LDA) for four different
models: two-lead ECG, three-lead ECG, a derived three-lead ECG and a factor analysis solution consisting of wavelet
characteristic loadings on the factors. A comparison was also made between wavelet characteristics derived form
individual P-waves verses wavelet characteristics derived from a signal-averaged P-wave for each participant. These
wavelet models were also compared to standard cardiological measures of duration, terminal force and duration
divided by the PR segment. Results for the individual P-wave approach generally outperformed the standard
cardiological measures and the signal-averaged P-wave approach. The best wavelet model on the basis of both
classification performance and simplicity was the two-lead model that uses leads II and V1. It was concluded that the
wavelet approach of automating classification is worth pursuing with larger samples to validate and extend the
present study.
http://kasanpro.com/ieee/final-year-project-center-tiruvallur-reviews