182 research outputs found
Analysis & Numerical Simulation of Indian Food Image Classification Using Convolutional Neural Network
Recognition of Indian food can be assumed to be a fine-grained visual task owing to recognition property of various food classes. It is therefore important to provide an optimized approach to segmentation and classification for different applications based on food recognition. Food computation mainly utilizes a computer science approach which needs food data from various data outlets like real-time images, social flat-forms, food journaling, food datasets etc, for different modalities. In order to consider Indian food images for a number of applications we need a proper analysis of food images with state-of-art-techniques. The appropriate segmentation and classification methods are required to forecast the relevant and upgraded analysis. As accurate segmentation lead to proper recognition and identification, in essence we have considered segmentation of food items from images. Considering the basic convolution neural network (CNN) model, there are edge and shape constraints that influence the outcome of segmentation on the edge side. Approaches that can solve the problem of edges need to be developed; an edge-adaptive As we have solved the problem of food segmentation with CNN, we also have difficulty in classifying food, which has been an important area for various types of applications. Food analysis is the primary component of health-related applications and is needed in our day to day life. It has the proficiency to directly predict the score function from image pixels, input layer to produce the tensor outputs and convolution layer is used for self- learning kernel through back-propagation. In this method, feature extraction and Max-Pooling is considered with multiple layers, and outputs are obtained using softmax functionality. The proposed implementation tests 92.89% accuracy by considering some data from yummly dataset and by own prepared dataset. Consequently, it is seen that some more improvement is needed in food image classification. We therefore consider the segmented feature of EA-CNN and concatenated it with the feature of our custom Inception-V3 to provide an optimized classification. It enhances the capacity of important features for further classification process. In extension we have considered south Indian food classes, with our own collected food image dataset and got 96.27% accuracy. The obtained accuracy for the considered dataset is very well in comparison with our foregoing method and state-of-the-art techniques.
Dense Visual Odometry Using Genetic Algorithm
Our work aims to estimate the camera motion mounted on the head of a mobile
robot or a moving object from RGB-D images in a static scene. The problem of
motion estimation is transformed into a nonlinear least squares function.
Methods for solving such problems are iterative. Various classic methods gave
an iterative solution by linearizing this function. We can also use the
metaheuristic optimization method to solve this problem and improve results. In
this paper, a new algorithm is developed for visual odometry using a sequence
of RGB-D images. This algorithm is based on a genetic algorithm. The proposed
iterative genetic algorithm searches using particles to estimate the optimal
motion and then compares it to the traditional methods. To evaluate our method,
we use the root mean square error to compare it with the based energy method
and another metaheuristic method. We prove the efficiency of our innovative
algorithm on a large set of images.Comment: 9 pages, 9 figure
Non-learning Stereo-aided Depth Completion under Mis-projection via Selective Stereo Matching
We propose a non-learning depth completion method for a sparse depth map
captured using a light detection and ranging (LiDAR) sensor guided by a pair of
stereo images. Generally, conventional stereo-aided depth completion methods
have two limiations. (i) They assume the given sparse depth map is accurately
aligned to the input image, whereas the alignment is difficult to achieve in
practice. (ii) They have limited accuracy in the long range because the depth
is estimated by pixel disparity. To solve the abovementioned limitations, we
propose selective stereo matching (SSM) that searches the most appropriate
depth value for each image pixel from its neighborly projected LiDAR points
based on an energy minimization framework. This depth selection approach can
handle any type of mis-projection. Moreover, SSM has an advantage in terms of
long-range depth accuracy because it directly uses the LiDAR measurement rather
than the depth acquired from the stereo. SSM is a discrete process; thus, we
apply variational smoothing with binary anisotropic diffusion tensor (B-ADT) to
generate a continuous depth map while preserving depth discontinuity across
object boundaries. Experimentally, compared with the previous state-of-the-art
stereo-aided depth completion, the proposed method reduced the mean absolute
error (MAE) of the depth estimation to 0.65 times and demonstrated
approximately twice more accurate estimation in the long range. Moreover, under
various LiDAR-camera calibration errors, the proposed method reduced the depth
estimation MAE to 0.34-0.93 times from previous depth completion methods.Comment: 15 pages, 13 figure
Online Targetless End-to-End Camera-LIDAR Self-calibration
In this paper we propose an end-to-end, automatic, online camera-LIDAR calibration approach, for application in self driving vehicle navigation. The main idea is to connect the image domain and the 3D space by generating point clouds from camera data while driving, using a structure from motion (SfM) pipeline, and use it as the basis for registration. As a core step of the algorithm we introduce an object level alignment to transform the generated and captured point clouds into a common coordinate system. Finally, we calculate the correspondences between the 2D image domain and the 3D LIDAR point clouds, to produce the registration. We evaluated the method in various different real life traffic scenarios
A Critical Review of Deep Learning-Based Multi-Sensor Fusion Techniques
In this review, we provide a detailed coverage of multi-sensor fusion techniques that use RGB stereo images and a sparse LiDAR-projected depth map as input data to output a dense depth map prediction. We cover state-of-the-art fusion techniques which, in recent years, have been deep learning-based methods that are end-to-end trainable. We then conduct a comparative evaluation of the state-of-the-art techniques and provide a detailed analysis of their strengths and limitations as well as the applications they are best suited for
An Efficient Point-Matching Method Based on Multiple Geometrical Hypotheses
Point matching in multiple images is an open problem in computer vision because of the numerous geometric transformations and photometric conditions that a pixel or point might exhibit in the set of images. Over the last two decades, different techniques have been proposed to address this problem. The most relevant are those that explore the analysis of invariant features. Nonetheless, their main limitation is that invariant analysis all alone cannot reduce false alarms. This paper introduces an efficient point-matching method for two and three views, based on the combined use of two techniques: (1) the correspondence analysis extracted from the similarity of invariant features and (2) the integration of multiple partial solutions obtained from 2D and 3D geometry. The main strength and novelty of this method is the determination of the point-to-point geometric correspondence through the intersection of multiple geometrical hypotheses weighted by the maximum likelihood estimation sample consensus (MLESAC) algorithm. The proposal not only extends the methods based on invariant descriptors but also generalizes the correspondence problem to a perspective projection model in multiple views. The developed method has been evaluated on three types of image sequences: outdoor, indoor, and industrial. Our developed strategy discards most of the wrong matches and achieves remarkable F-scores of 97%, 87%, and 97% for the outdoor, indoor, and industrial sequences, respectively
Advanced Signal Processing Techniques Applied to Power Systems Control and Analysis
The work published in this book is related to the application of advanced signal processing in smart grids, including power quality, data management, stability and economic management in presence of renewable energy sources, energy storage systems, and electric vehicles. The distinct architecture of smart grids has prompted investigations into the use of advanced algorithms combined with signal processing methods to provide optimal results. The presented applications are focused on data management with cloud computing, power quality assessment, photovoltaic power plant control, and electrical vehicle charge stations, all supported by modern AI-based optimization methods
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