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Flower Species recognition System using Convolution Neural Networks and Transfer learning 4

Flower Species Recognition System using Convolution Neural N...

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4th international conference on Signal processing, Communication and Networking (ICSCN)

作者： Gogul, I. Kumar, V. Sathiesh Anna Univ Madras Inst Technol Dept Elect Engn Madras 600044 Tamil Nadu India

ISBN: (纸本)9781509047406

Automatic identification and recognition of medicinal plant species in environments such as forests, mountains and dense regions is necessary to know about their existence. In recent years, plant species recognition is carried out based on the shape, geometry and texture of various plant parts such as leaves, stem, flowers etc. Flower based plant species identification systems are widely used. While modern search engines provide methods to visually search for a query image that contains a flower, it lacks in robustness because of the intra-class variation among millions of flower species around the world. Hence in this proposed research work, a Deep learning approach using Convolutional Neural Networks (CNN) is used to recognize flower species with high accuracy. images of the plant species are acquired using the built-in camera module of a mobile phone. Feature extraction of flower images is performed using a Transfer learning approach (i.e. extraction of complex features from a pre-trained network). A machine learning classifier such as Logistic Regression or Random Forest is used on top of it to yield a higher accuracy rate. This approach helps in minimizing the hardware requirement needed to perform the computationally intensive task of training a CNN. It is observed that, CNN combined with Transfer learning approach as feature extractor outperforms all the handcrafted feature extraction methods such as Local Binary pattern (LBP), Color Channel statistics, Color Histograms, Haralick Texture, Hu Moments and Zernike Moments. CNN combined with Transfer learning approach yields impressive Rank-1 accuracies of 73.05%, 93.41% and 90.60% using OverFeat, Inception-v3 and Xception architectures, respectively as Feature Extractors on FLOWERS102 dataset.

关键词： Deep learning Artificial Intelligence Convolutional Neural Networks Transfer learning Flower recognition

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Circular Foreign Object Detection in Chest X-ray images 1st

Circular Foreign Object Detection in Chest X-ray Images

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1st international conference on Recent Trends in image processing and pattern recognition (RTIP2R)

作者： Zohora, Fatema Tuz Santosh, K. C. Univ South Dakota Dept Comp Sci 414 E Clark St Vermillion SD 57069 USA

ISBN: (纸本)9789811048593;9789811048586

In automated chest X-ray screening (to detect i.e., Tuberculosis for instance), the presence of foreign objects (buttons, medical devices) hinders it's performance. In this paper, we present a new technique for detecting circular foreign objects, in particular buttons, within chest X-ray (CXR) images. In our technique, we use a pre-processing step that enhances the CXRs. Using these enhanced images, we find the edge images performing four different edge detection algorithms (Sobel, Canny, Prewitt, and Roberts) and after that, we apply some morphological operations to select candidates (image segmentation) in the chest region. Finally, we apply circular Hough transform (CHT) to detect the circular foreign objects on those images. In all tests, our algorithm performed well under a variety of CXRs. We also compared our proposed technique's performance with existing techniques in literature (Viola-Jones and CHT). Our technique was able to excel performance in terms of both detection accuracy and computational time.

关键词： Chest X-ray (CXR) images Foreign object detection Edge detection Button detection Circular Hough Transform (CHT) Viola-Jones

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Detection of Hypertension Retinopathy Using Deep learning and Boltzmann machines 1

Detection of Hypertension Retinopathy Using Deep Learning an...

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1st international conference on Computing and Applied Informatics (ICCAI) - Applied Informatics toward Smart Environment, People, and Society

作者： Triwijoyo, B. K. Pradipto, Y. D. BINUS Univ Jakarta Comp Sci Jakarta Indonesia BINUS Univ Jakarta Dept Psychol Fac Humanities Jakarta Indonesia

hypertensive retinopathy (HR) in the retina of the eye is disturbance caused by high blood pressure disease, where there is a systemic change of arterial in the blood vessels of the retina. Most heart attacks occur in patients caused by high blood pressure symptoms of undiagnosed. Hypertensive retinopathy Symptoms such as arteriolar narrowing, retinal haemorrhage and cotton wool spots. Based on this reasons, the early diagnosis of the symptoms of hypertensive retinopathy is very urgent to aim the prevention and treatment more accurate. This research aims to develop a system for early detection of hypertension retinopathy stage. The proposed method is to determine the combined features artery and vein diameter ratio (AVR) as well as changes position with Optic Disk (OD) in retinal images to review the classification of hypertensive retinopathy using Deep Neural Networks (DNN) and Boltzmann machines approach We choose this approach of because based on previous research DNN models were more accurate in the image pattern recognition, whereas Boltzmann machines selected because It requires speedy iteration in the process of learning neural network. The expected results from this research are designed a prototype system early detection of hypertensive retinopathy stage and analysed the effectiveness and accuracy of the proposed methods.

关键词： Deep neural networks

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Gait recognition-Based Human Identification and Gender Classification

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1st international conference on Computer Vision and image processing (CVIP)

作者： Arivazhagan, S. Induja, P. Mepco Schlenk Engn Coll Dept ECE Sivakasi India

ISBN: (纸本)9789811021046;9789811021039

The main objective of this work is to identify the persons and to classify the gender of those persons with the help of their walking styles from the gait sequences with arbitrary walking directions. The human silhouettes are extracted from the given gait sequences using background subtraction technique. Median value approach is used for the background subtraction. After the extraction of the silhouettes, the affinity propagation clustering is performed to group the silhouettes with similar views and poses to one cluster. The cluster-based averaged gait image is taken as a feature for each cluster. To learn the distance metric, sparse reconstruction-based metric learning has been used. It minimizes the intraclass sparse reconstruction errors and maximizes the interclass reconstruction errors simultaneously. The above-mentioned steps have come under the training phase. With the help of the metric learned in the training and the feature extracted from the testing video sequence, sparse reconstruction-based classification has been performed for identifying the person and gender classification of that person. The accuracy achieved for the human identification and gender classification is promising.

关键词： Affinity propagation (AP) Cluster-based averaged gait image (C-AGI) Sparse reconstruction-based metric learning (SRML) And sparse reconstruction-based classification (SRC)

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Word Retrieval from Kannada Document images Using HOG and Morphological Features 1st

Word Retrieval from Kannada Document Images Using HOG and Mo...

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1st international conference on Recent Trends in image processing and pattern recognition (RTIP2R)

作者： Hangarge, Mallikarjun Veershetty, C. Rajmohan, P. Mukarambi, Gururaj Karnatak Coll PG Dept Comp Sci Bidar India Gulbarga Univ Dept Comp Sci Kalaburagi India

ISBN: (纸本)9789811048593;9789811048586

This paper presents a method to retrieve words from Kannada documents. It works on Histogram of Oriented Gradients (HOG) and Morphological filters. A large dataset of 50000 words is created using 250 document pages belongs to different categories. A preprocessed document image is segmented using simple morphological filters. The histogram channels are designed over four-sided cells (i.e. R-HOG) to compute gradients of a word image. In parallel, morphological erosion, opening, top and bottom hat transformations are applied on each word. The densities of the resultant images are estimated. Later on, HOG and morphological features are fused. Then, the cosine distance is used to measure the similarity between two words i.e., query and candidate word, based on it, the relevance of the word is estimated by generating distance ranks. Then correctly matched words are selected at threshold 98%. The experimental results confirm the efficiency of our proposed method in terms of the average precision rate 91.23%, and average recall rate 84.78% as well as average F-measure 89.47%.

关键词： Word retrieval Cosine distance Digital image processing (DIP) image retrieval (IR)

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An Integrated Framework to image Retrieval Using L*a*b Color Space and Local Binary pattern 1st

An Integrated Framework to Image Retrieval Using L*a*b Color...

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1st international conference on Recent Trends in image processing and pattern recognition (RTIP2R)

作者： Neelima, N. Rao, P. Koteswara Praneeth, N. Sai Mamatha, G. N. Jain Univ Bangalore 562112 Karnataka India

ISBN: (纸本)9789811048593;9789811048586

Information retrieval in the form of documents, images is playing a key role in day-to-day life of humans. Real world applications scenario is changing daily, so as the improvement is also becomes a necessary. Various approaches were proposed for retrieval, but all the input images were considered under proper illumination conditions. If the images suffered with illumination angle color and viewing angle changes then it's very difficult to retrieve similar images. We propose a system which can deal with illumination angle and color variations. Experiments were conducted on ALOI (Amsterdam Library of Object images) dataset, which is a collection of one thousand objects each with hundred similar images. These images were recorded under changing the illumination angle, color and viewing angle. Experimental results prove that the proposed approach outperforms well in terms of retrieval efficiency ...

关键词： Segmentation L*a*b color space Similarity matching Cityblock distance

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Iris Features Segmentation for Arabian Horses Identification 17

Iris Features Segmentation for Arabian Horses Identification

引用

international conference on Internet of Things and machine learning (IML)

作者： Salama, Aya Hassanien, Aboul Ellah Fahmy, Aly Cairo Univ Fac Comp Sci & Informat Technol Cairo Egypt

ISBN: (纸本)9781450352437

This paper introduces a new approach for segmentation of the found significant features in the iris of Arabian horses. It was found that the most significant features are as follows;the retina cogina, the pupil shape and the pupil size. Accordingly;a new technique that depends on segmentation of the pupil region with the retina cogina in the iris is proposed. Circular hough transform, canny edge detection, and k-mean clustering are applied. For noise removal, connected component labeling and morphological analysis take place. The presented study surveys the application of the proposed technique on collected data set of iris images for different 145 Arabian horses, For testing the effectiveness and the success of such newly applied technique, Jaccard similarity coefficient was evaluated and it was found in the range 80% to 95%. Hence;such technique may represent a new contribution in the iris identification as it hasnfit been applied in any previous study.

关键词： Haugh transform canny edge detection feature vectors gabor wavelet connected components labeling morphological image processing and SWARM

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A Graph-Based Active learning Approach Using Forest Classifier for image Retrieval 1st

A Graph-Based Active Learning Approach Using Forest Classifi...

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1st international conference on Data Engineering and Communication Technology (ICDECT)

作者： Dhawale, Shrikant Joglekar, Bela Kulkarni, Parag Maharashtra Inst Technol Dept Informat Technol Pune Maharashtra India Bharti Vidyapeeth Univ Pune Maharashtra India Anomaly Solut Pvt Ltd Pune Maharashtra India

ISBN: (纸本)9789811016783;9789811016776

Content-Based image Retrieval System is comprised of large image collections which find their use in applications such as statistical analysis, medical diagnosis, photograph archiving, crime prevention, face detection, etc. This poses a challenge for pattern recognition techniques used in image retrieval, which require being both efficient and effective. These techniques involve high computational burden in training phase, to separate samples from distinct classes, for active learning. In active learning paradigm, system first returns a small image set. Inference is drawn from this image set based on relevance as per user perception. Hence image retrieval based on context suffers in terms of precision and recall, as retraining and interactive time response is involved. A classifier known as Optimum-Path Forest (OPF) reduces this computational overhead by transforming the problem of classification as a graph obtained from dataset samples in feature space. It involves fast computation of trees built through the forest classifier in a graph resulting from training dataset samples. An optimum path is conquered from least cost approximation of a shortest path through the current prototype sample to the image under consideration.

关键词： Forest classifier Active learning Minimum spanning tree image retrieval

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A Non-Invasive 2D Digital Imaging Method for Detection of Surface Lesions Using machine learning

A Non-Invasive 2D Digital Imaging Method for Detection of Su...

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international conference on Cyberworlds (CW)

作者： Hussain, Nosheen Cooper, Patricia Shnyder, steven Ugail, Hassan Bukar, Ali Connah, David Univ Bradford Inst Canc Therapeut Bradford BD7 1DP W Yorkshire England Univ Bradford Ctr Visual Comp Bradford BD7 1DP W Yorkshire England

ISBN: (纸本)9780769562155

As part of the cancer drug development process, evaluation in experimental subcutaneous tumour transplantation models is a key process. This involves implanting tumour material underneath the mouse skin and measuring tumour growth using calipers. This methodology has been proven to have poor reproducibility and accuracy due to observer variation. Furthermore the physical pressure placed on the tumour using calipers is not only distressing for the mouse but could also lead to tumour damage. Non-invasive digital imaging of the tumour would reduce handling stresses and allow volume determination without any potential tumour damage. This is challenging as the tumours sit under the skin and have the same colour pattern as the mouse body making them hard to differentiate in a 2D image. We used the pre-trained convolutional neural network VGG-16 and extracted multiple layers in an attempt to accurately locate the tumour. When using the layer FC7 after RELU activation for extraction, a recognition rate of 89.85% was achieved.

关键词： image processing Skin Detection Subcutaneous Tumour machine learning

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Skew Detection and Correction of Devanagari Script Using Interval Halving Method 1st

Skew Detection and Correction of Devanagari Script Using Int...

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1st international conference on Recent Trends in image processing and pattern recognition (RTIP2R)

作者： Jundale, Trupti A. Hegadi, Ravindra S. Solapur Univ Dept Comp Sci Solapur 413255 India

ISBN: (纸本)9789811048593;9789811048586

We proposed a method for skew detection and correction of handwritten and printed Devanagari script which is based on pixels of axes parallel rectangle and interval halving also called bisection method. The proposed approach works for skewed word, uniform skewed angle of line and paragraph. The method uses tangential pixels of axes parallel rectangle. By finding these pixels, area of a rectangle which is axes parallel is calculated. Then using numerical bisection method, pixels of this rectangle box are rotated and again area of the rectangle using new rotated pixels is calculated and the calculated area is compared with the previously calculated area. This process continues till we cant get the least area of axes parallel rectangle. The angle with the least area of a rectangle is the skew angle. This technique achieves a good result as compared with other techniques in the literature with the accuracy rate of 95%. The manual trained dataset is used for testing purpose to calculate the accuracy.

关键词： Pre-processing Axes-parallel rectangle Interval halving method Skew detection Skew correction

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