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Plants are to be considered as one of the important things that plays a very essential role for all living beings exists on earth. But due to some unawareness and environment deterioration, some very rare plants are on the verge of extinction. Knowledge of rare leaves used for medicine and other plants is very critical in future. Leaf identification and classification plays a vital role for plant...
In this article, we present our work on classifier to realize a Wireless Capsule Endoscopy (WCE) including a Smart Vision Chip (SVC). Our classifier is based on fuzzy tree and forest of fuzzy trees. We obtain a sensitivity of 92.80% and a specificity of 91.26% with a false detection rate of 8.74% on a large database, that we have constructed, composed of 18910 images containing 3895 polyps from 20...
This paper presents a methodology for recognition of handwritten Marathi and English Characters-Numerals using shape context descriptor. During pre-processing an algorithm is developed to extract the Marathi and English Characters-Numerals form grid formatted datasheets. The corresponding sample points around the boundary of a character are computed. This is followed by obtaining the centroid of the...
This paper present an automatic 3D liver segmentation based on Active Shape Model. It allows us to introduce a 3D modeling feature for the target organ to lead the segmentation. This method is tested on the dataset IRCAD which containe a 20 Computed tomography exams. These exams are obtained with different scanning protocol. Thence, we used two algorithms. First, we employed the Shape Context based...
Plants are sine-qua-non for existence of human life. The benefits provided by plants are manifold. Plant identification is challenging but it is extremely useful for making accurate decisions regarding livestock systems, conservation and ecology. Though most plants may look similar, they might not be the same. Hence, it becomes essential to develop a system which will identify plants by studying the...
Despite of having no explicit shape model, multi-atlas approaches to image segmentation have proved to be a top-performer for several diverse datasets and imaging modalities. In this paper, we show how one can directly incorporate shape regularization into the multi-atlas framework. Unlike traditional methods, our proposed approach does not rely on label fusion on the voxel level. Instead, each registered...
As an artificial intelligent technique, artificial neural networks (ANNs) have been applied successfully in a wide range of fields due to its effective learning ability. In this paper, we conduct an empirical application on fragrance bottle form design due to its wide variety of appearances. For getting a better structure of the ANN model to develop the consumer-oriented expert system, we conduct...
CT image based lung nodule detection is the most widely used and accepted method for detecting lung cancer. Most CT image based methods are based on supervised/unsupervised learning, which has a high number of false positives and needs a large amount pre-segmented training samples. This problem can be solved, if a set of optimally small number of training samples can be created, where each sample...
We present a novel method for training (evolving) fully convolutional neural networks (CNNs) for deformable object manipulation. Instead of using a weight update rule, we evolve an ensemble of compositional pattern generating networks (CPPNs) by means of a genetic algorithm (GA). These ensembles generate the convolutional kernels that comprise the CNN. This allows the GA to search for fit kernels...
Existing distance metric learning methods define an objective function and seek a distance metric (or equivalently a projection) that minimizes it. In this paper, we propose a different approach that illustrates how to formulate distance metric learning as a regression problem. First, the objective function is minimized to learn target representations. Then, a regression method is employed to learn...
In this paper we propose a multi-modal object recognition system that uses a two-step hypothesis verification approach to improve runtime efficiency. The system uses local and global appearance and shape features, generating many possibly competing hypotheses, which are then verified such that the scene can be optimally explained in terms of recognized object models. The introduced modification in...
Face alignment is an important issue in many computer vision problems. The key problem is to find the nonlinear mapping from face image or feature to landmark locations. In this paper, we propose a novel cascaded approach with bidirectional Long Short Term Memory (LSTM) neural networks to approximate this nonlinear mapping. The cascaded structure is used to reduce the complexity of this problem and...
We present an approach for unsupervised computation of local shape descriptors, which relies on the use of linear autoencoders for characterizing local regions of complex shapes. The proposed approach responds to the need for a robust scheme to index binary images using local descriptors, which arises when only few examples of the complete images are available for training, thus making inaccurate...
Multimedia education is playing a significant and increasing role for education purposes, thus leading to a large number of electronic documents. Plane geometry figures (PGFs), as important components of these documents, are regarded as very helpful information to most retrieval systems in the field of mathematics education. However, the burdensome work of annotation has become one of the chief obstacles...
We propose a novel supervised initialization scheme for cascaded face alignment by searching nearest neighbors based on global image descriptors. Unlike existing schemes which resort to additional large training data sets for learning features, our method does not require additional training steps; thus making our method low computational. Moreover, we found that it is sufficient to use a simple low-dimensional...
Institutes and libraries around the globe are preserving the literary heritage by digitizing historical documents. However, to make this data easily accessible the scanned documents need to be transformed into search-able text. State of the art OCR systems using Long-Short-Term-Memory networks (LSTM) have been applied successfully to recognize text in both printed and handwritten form. Besides the...
Recognition of fruits automatically using machine vision is considered as challenging task as fruits exist in various colors, sizes, shapes and textures. Additionally, when images are acquired of them, variation is introduced due to imaging conditions also. In this paper we have recognized nine different classes of fruits. Fruit image dataset are obtained from web as well as certain images are acquired...
Cascade regression framework has been successfully applied to facial landmark detection and achieves state-of-the-art performance recently. It requires large number of facial images with labeled landmarks for training regression models. We propose to use cascade regression framework to detect eye center by capturing its contextual and shape information of other related eye landmarks. While for eye...
Imitation cartoon drawing is an important skill for cartoonists, requiring quantity of efforts on practising and guidance. In this paper, we propose EvaToon, an imitated drawing evaluate system, which automatically assigns judging scores and marks improper drawing regions. With our system, cartoonists can practise and get guidance by themselves. We have cooperated with several experts on developing...
It has been shown that significant age difference between a probe and gallery face image can decrease the matching accuracy. If the face images can be normalized in age, there can be a huge impact on the face verification accuracy and thus many novel applications such as matching driver's license, passport and visa images with the real person's images can be effectively implemented. Face progression...
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