Histogram of distances for local surface description

3D object recognition is proven superior compared to its 2D counterpart with numerous implementations, making it a current research topic. Local based proposals specifically, although being quite accurate, they limit their performance on the stability of their local reference frame or axis (LRF/A) on which the descriptors are defined. Additionally, extra processing time is demanded to estimate the LRF for each local patch. We propose a 3D descriptor which overrides the necessity of a LRF/A reducing dramatically processing time needed. In addition robustness to high levels of noise and non-uniform subsampling is achieved. Our approach, namely Histogram of Distances is based on multiple L2-norm metrics of local patches providing a simple and fast to compute descriptor suitable for time-critical applications. Evaluation on both high and low quality popular point clouds showed its promising performance

Improvement of Corner Detection Algorithms (Harris, FAST and SUSAN) Based on Reduction of Features Space and Complexity Time

The active detection for gratifying features can be a definitive pace for computer vision in different tasks. Corners become more preferable models because of their two dimensional constrain; two dimensional limitations and algorithms can be rapid to detect them. Corners in images form significant information. Elicitation corners precisely are significant for processing image data to minimize a lot of computations. This paper can be used three vastly algorithms for detection the corner in images improvement Harris, improvement FAST, and improvement SUSAN which are based on two criteria for comparison to minimize the space of interest features and runtime reduction. From that, it can conclude that the algorithm of improvement FAST was outstanding to improvement Harris and improvement SUSAN algorithms on these criteria. FAST, SUSAN and Harris algorithms for corner detected were improved by applying Haar transform and choosing an adaptive gray difference threshold. Improvement FAST, has been offered which can be exceeded the previous two algorithms, improvement Harris and improvement SUSAN in both less run time and small features space. For example, the time taken by car image is 0.0005 second to extract the features using improvement FAST algorithm, which is much less than that used by the SUSAN and Harris algorithms. Improvement Harris takes 0.0074second and SUSAN takes 0.0096 second

3D Automatic Target Recognition for Future LIDAR Missiles

We present a real-time three-dimensional automatic target recognition approach appropriate for future light detection and ranging-based missiles. Our technique extends the speeded-up robust features method into the third dimension by solving multiple two-dimensional problems and performs template matching based on the extreme case of a single pose per target. Evaluation on military targets shows higher recognition rates under various transformations and perturbations at lower processing time compared to state-of-the-art approaches

Модель визначення популярності домашньої тварини за фотографією на основі глибоких нейронних мереж

Магістерська дисертація: 111 с., 40 рис., 21 табл., 1 дод., 38 джерел. Дана робота присвячена дослідженню методів вирішення задачі регресії на фотографії. Об’єктом дослідження є набір фотографій домашніх тварин, їх метадані, а також штучні згорткові нейронні мережі. Предметом дослідження є прогнозування неперервної зміни популярності фотографії домашньої тварини. Метою дослідження є аналіз та підвищення якості роботи та вибір найкращого рішення серед обраних кандидатів для вирішення поставленої задачі. Актуальність роботи полягає в тому, що наразі кількість домашніх тварин в притулках зростає, а одним з факторів усиновлення тварин є перший погляд на її фото на веб сторінці притулку. Наразі вибір найкращого фото в більшості випадків робиться у ручному режимі, без інтелектуальних методів аналізу та машинного навчання для автоматизації процесу та відкидання людського фактору. Результатом роботи є розроблена модель, що може прогнозувати цільову зміну клікабельності фотографії домашньої тварини.Master’s thesis: 111 p., 40 fig., 21 tabl., 1 appendix., 38 ref. This work is devoted to the study of methods for solving the regression problem in photography. The object of the study is a set of photos of pets, their metadata, as well as artificial convolutional neural networks. The subject of the research is to predict the continuous variable in the popularity of pet photography. The aim of the study is to analyze and improve the quality of the model and choose the best solution among the selected candidates to solve the problem. The urgency of the work is that currently the number of pets in shelters is growing, and one of the factors in the adoption of animals is the first look at its photo on the website of the shelter. Currently, the selection of the best photo in most cases is done manually, without intelligent methods of analysis and machine learning to automate the process and reject the human factor. The result is a developed model that can predict the target variable in the clickability of a photograph of a pet