An end-to-end convolutional selective autoencoder approach to Soybean Cyst Nematode eggs detection

This paper proposes a novel selective autoencoder approach within the framework of deep convolutional networks. The crux of the idea is to train a deep convolutional autoencoder to suppress undesired parts of an image frame while allowing the desired parts resulting in efficient object detection. The efficacy of the framework is demonstrated on a critical plant science problem. In the United States, approximately $1 billion is lost per annum due to a nematode infection on soybean plants. Currently, plant-pathologists rely on labor-intensive and time-consuming identification of Soybean Cyst Nematode (SCN) eggs in soil samples via manual microscopy. The proposed framework attempts to significantly expedite the process by using a series of manually labeled microscopic images for training followed by automated high-throughput egg detection. The problem is particularly difficult due to the presence of a large population of non-egg particles (disturbances) in the image frames that are very similar to SCN eggs in shape, pose and illumination. Therefore, the selective autoencoder is trained to learn unique features related to the invariant shapes and sizes of the SCN eggs without handcrafting. After that, a composite non-maximum suppression and differencing is applied at the post-processing stage.Comment: A 10 pages, 8 figures International Conference on Machine Leaning(ICML) Submissio

Perbandingan Metode ResNet, YoloV3, dan TinyYoloV3 pada Deteksi Citra dengan Pemrograman Python

Pengolahan citra (image processing) adalah teknik mengolah citra yang mentransformasikan citra masukan menjadi citra lain agar keluaran memiliki kualitas yang lebih baik dibandingkan kualitas citra masukan. Deteksi objek dalam digital image processing adalah suatu proses yang digunakan untuk menentukan keberadaan objek tertentu di dalam suatu citra digital. Proses deteksi tersebut dapat dilakukan dengan berbagai macam metode yang umumnya melakukan pembacaan fitur-fitur dari seluruh objek pada citra input. Pada pendeteksian objek tersebut terdapat beberapa metode beberapa diantaranya yaitu metode ResNet, YOLOv3 dan TinyYOLOv3. Dalam penelitian ini akan dilakukan perbedaan ketiga metode tersebut. Tujuan dari ujicoba dalam penelitian ini adalah mengetahui lebih jauh hasil deteksi objek pada citra dengan tingkat keakuratan yang baik. Dan penelitian ini dapat membutkikan bahwa hasil pendeteksian objek dengan menggunakan model Yolov3, hasil keakuratannya lebih tinggi dibandingkan dengan model ResNet dan model Tiny Yolo

Content base Image Retrival using Color Histogram and Global Features

In the world we live in today we can safely say that the internet revolution, digital representation and transportation of data had offered efficient solution for information delivery. But these phenomenal developments brought their own concern and problems in area of searching, security, monitoring and use of information by qualified end users. In early days the information was generally obtained and processed in the form of text but nowadays people are aware of the truth that information can be obtained in the form of graphics, which are a more precise and a more inclusive illustration of information. In this paper a new technique is introduced for fast Content based Image Retrieval with the help of colors and global features. Image retrieval on the base of color histogram is a very useful and common technique but in some cases this technique is not reliable. In proposed technique some new things are added with the color histogram to make it more efficient and reliable for Content based image retrieval

Anomaly Detection in the Molecular Structure of Gallium Arsenide Using Convolutional Neural Networks

This paper concerns the development of a machine learning tool to detect anomalies in the molecular structure of Gallium Arsenide. We employ a combination of a CNN and a PCA reconstruction to create the model, using real images taken with an electron microscope in training and testing. The methodology developed allows for the creation of a defect detection model, without any labeled images of defects being required for training. The model performed well on all tests under the established assumptions, allowing for reliable anomaly detection. To the best of our knowledge, such methods are not currently available in the open literature; thus, this work fills a gap in current capabilities

A study of feature combination for vehicle detection based on image processing

Video analytics play a critical role in most recent traffic monitoring and driver assistance systems. In this context, the correct detection and classification of surrounding vehicles through image analysis has been the focus of extensive research in the last years. Most of the pieces of work reported for image-based vehicle verification make use of supervised classification approaches and resort to techniques, such as histograms of oriented gradients (HOG), principal component analysis (PCA), and Gabor filters, among others. Unfortunately, existing approaches are lacking in two respects: first, comparison between methods using a common body of work has not been addressed; second, no study of the combination potentiality of popular features for vehicle classification has been reported. In this study the performance of the different techniques is first reviewed and compared using a common public database. Then, the combination capabilities of these techniques are explored and a methodology is presented for the fusion of classifiers built upon them, taking into account also the vehicle pose. The study unveils the limitations of single-feature based classification and makes clear that fusion of classifiers is highly beneficial for vehicle verification

Content base Image Retrival using Color Histogram and Global Features

In the world we live in today we can safely say that the internet revolution, digital representation and transportation of data had offered efficient solution for information delivery. But these phenomenal developments brought their own concern and problems in area of searching, security, monitoring and use of information by qualified end users. In early days the information was generally obtained and processed in the form of text but nowadays people are aware of the truth that information can be obtained in the form of graphics, which are a more precise and a more inclusive illustration of information. In this paper a new technique is introduced for fast Content based Image Retrieval with the help of colors and global features. Image retrieval on the base of color histogram is a very useful and common technique but in some cases this technique is not reliable. In proposed technique some new things are added with the color histogram to make it more efficient and reliable for Content based image retrieval

Robust Intensity-Based Localization Method for Autonomous Driving on Snow-Wet Road Surface

金沢大学新学術創成研究機構Autonomous vehicles are being developed rapidly in recent years. In advance implementation stages, many particular problems must be solved to bring this technology into the market place. This paper focuses on the problem of driving in snow and wet road surface environments. First, the quality of laser imaging detection and ranging (LIDAR) reflectivity decreases on wet road surfaces. Therefore, an accumulation strategy is designed to increase the density of online LIDAR images. In order to enhance the texture of the accumulated images, principal component analysis is used to understand the geometrical structures and texture patterns in the map images. The LIDAR images are then reconstructed using the leading principal components with respect to the variance distribution accounted by each eigenvector. Second, the appearance of snow lines deforms the expected road context in LIDAR images. Accordingly, the edge profiles of the LIDAR and map images are extracted to encode the lane lines and roadside edges. Edge matching between the two profiles is then calculated to improve localization in the lateral direction. The proposed method has been tested and evaluated using real data that are collected during the winter of 2016-2017 in Suzu and Kanazawa, Japan. The experimental results show that the proposed method increases the robustness of autonomous driving on wet road surfaces, provides a stable performance in laterally localizing the vehicle in the presence of snow lines, and significantly reduces the overall localization error at a speed of 60 km/h. © 2005-2012 IEEE

A Study of Feature Combination for Vehicle Detection Based on Image Processing

Video analytics play a critical role in most recent traffic monitoring and driver assistance systems. In this context, the correct detection and classification of surrounding vehicles through image analysis has been the focus of extensive research in the last years. Most of the pieces of work reported for image-based vehicle verification make use of supervised classification approaches and resort to techniques, such as histograms of oriented gradients (HOG), principal component analysis (PCA), and Gabor filters, among others. Unfortunately, existing approaches are lacking in two respects: first, comparison between methods using a common body of work has not been addressed; second, no study of the combination potentiality of popular features for vehicle classification has been reported. In this study the performance of the different techniques is first reviewed and compared using a common public database. Then, the combination capabilities of these techniques are explored and a methodology is presented for the fusion of classifiers built upon them, taking into account also the vehicle pose. The study unveils the limitations of single-feature based classification and makes clear that fusion of classifiers is highly beneficial for vehicle verification