Object Segmentation from Background of 2D Image

واحدة من المهام الصعبة في مجال معالجة الصور ولا تزال لم تحل هو تجزئة الكائن من خلفيته بدقة. لذلك، فإن هذا العمل يهتم باقتراح طريقة جديدة لغرض تجزئة الكائن من خلفيته لغرض تحسين الصور والحصول على خصائص الكائن بدون بقية مناطق الصورة. هذه العملية مهمة لتوفير التصنيف الأمثل في عملية التعرف على الأنماط. لذلك، تم اقتراح الأسلوب الذي يتضمن عدة مهام، بعد تحميل الملفات الستة من الصور. تم تطبيق خوارزمية تجزئة اعتمادا على الحدود ولون الكائن. وأخيرا، تم استخدام خوارزمية التصفية المتوسطة لإزالة الأجسام الغير مرغوب بها من مختلف الأشكال والأحجام. تم اختبار الخوارزمية على صور متنوعة، وكانت النتائج ذات دقة عالية. بعبارة أخرى، فإن الطريقة المقترحة قادرة على تقسيم الكائنات من الخلفية مع نتائج واعدة.  One of the difficult tasks in the image processing field and still not solved is segmentation of object from background of 2D image accurately. Therefore, a new method has been proposed for the purpose of segmenting the object from its background for the purpose of enhancing the images and obtains characteristics of the object without the rest of the region of the image. This process is important to provide optimal classification in the process of pattern recognition. Therefore, this paper proposed the method that includes several tasks, after loading the six files of images; this work applies the segmentation al- gorithm depending on the border and the color of the object. Finally, 2D median filtering algorithm was employed to remove noisy objects of various shapes and sizes. The algo- rithm was tested on variety images, and the results are high precision. In the other words, the proposed method is able to segment the objects from the background with promising results

Unified Heat Kernel Regression for Diffusion, Kernel Smoothing and Wavelets on Manifolds and Its Application to Mandible Growth Modeling in CT Images

We present a novel kernel regression framework for smoothing scalar surface data using the Laplace-Beltrami eigenfunctions. Starting with the heat kernel constructed from the eigenfunctions, we formulate a new bivariate kernel regression framework as a weighted eigenfunction expansion with the heat kernel as the weights. The new kernel regression is mathematically equivalent to isotropic heat diffusion, kernel smoothing and recently popular diffusion wavelets. Unlike many previous partial differential equation based approaches involving diffusion, our approach represents the solution of diffusion analytically, reducing numerical inaccuracy and slow convergence. The numerical implementation is validated on a unit sphere using spherical harmonics. As an illustration, we have applied the method in characterizing the localized growth pattern of mandible surfaces obtained in CT images from subjects between ages 0 and 20 years by regressing the length of displacement vectors with respect to the template surface.Comment: Accepted in Medical Image Analysi

NeatVision: a development environment for machine vision engineers

This Chapter will detail a free image analysis development environment for machine vision engineers. The environment provides high-level access to a wide range of image manipulation, processing and analysis algorithms (over 300 to date) through a well-defined and easy to use graphical interface. Users can extend the core library using the developer’s interface, a plug-in, which features, automatic source code generation, compilation with full error feedback and dynamic algorithm updates. The Chapter will also discuss key issues associated with the environment and outline the advantages in adopting such a system for machine vision application developmen

Design Of Efficient Baseline Coders For Image Compression

Image compression is the process of reducing the number of bits required to represent an image. This can be achieved by reducing (or ideally, eliminating) various types of redundancy that exist in the imaging data. This research develops various types of down-sampling filters to compress the image and followed by up-sampling filters to decompress the image. The concept used to down-sample is by deleting either the odd or even numbered columns or rows. Besides that, the columns and rows are also deleted in two’s to further compress the image. The image is then up-sampled by using duplication or replacing the deleted row or columns with the average of other rows or columns. The compression ratio achieved through this research is 50% and 67%. The images are then compared by the Peak Signal-to-Noise Ratio and through observation to decide on the best type of filter. Through the research and results, it is proved that the filter that up-samples the image by replacing the deleted rows with the average of the two subsequent even numbered row has the best output

Exploring the potential of NIR hyperspectral imaging for automated quantification of rind amount in grated Parmigiano Reggiano cheese

Parmigiano Reggiano (P-R) is one of the most important Italian food products labelled with Protected Designation of Origin (PDO). The PDO denomination is applied also to grated P-R cheese products meeting the requirements regulated by the Specifications of Parmigiano Reggiano Cheese. Different quality parameters are monitored, including the percentage of rind, which is edible and should not exceed the limit of 18% (w/w). The present study aims at evaluating the possibility of using near infrared hyperspectral imaging (NIR-HSI) to quantify the rind percentage in grated Parmigiano Reggiano cheese samples in a fast and non-destructive manner. Indeed, NIR-HSI allows the simultaneous acquisition of both spatial and spectral information from a sample, which is more suitable than classical single-point spectroscopy for the analysis of heterogeneous samples like grated cheese. Hyperspectral images of grated P-R cheese samples containing increasing levels of rind were acquired in the 900–1700 nm spectral range. Each hyperspectral image was firstly converted into a one-dimensional signal, named hyperspectrogram, which codifies the relevant information contained in the image. Then, the matrix of hyperspectrograms was used to calculate a calibration model for the prediction of the rind percentage using Partial Least Squares (PLS) regression. The calibration model was validated considering two external test sets of samples, confirming the effectiveness of the proposed approach

Occupancy grid mapping for rover navigation based on semantic segmentation

Obstacle mapping is a fundamental building block of the autonomous navigation pipeline of many robotic platforms such as planetary rovers. Nowadays, occupancy grid mapping is a widely used tool for obstacle perception. It foreseen the representation of the environment in evenly spaced cells, whose posterior probability of being occupied is updated based on range sensors measurement. In more classic approaches, the cells are updated to occupied at the point where the ray emitted by the range sensor encounters an obstacle, such as a wall. The main limitation of this kind of methods is that they are not able to identify planar obstacles, such as slippery, sandy, or rocky soils. In this work, we use the measurements of a stereo camera combined with a pixel labeling technique based on Convolution Neural Networks to identify the presence of rocky obstacles in planetary environment. Once identified, the obstacles are converted into a scan-like model. The estimation of the relative pose between successive frames is carried out using ORB-SLAM algorithm. The final step consists of updating the occupancy grid map using the Bayes' update Rule. To evaluate the metrological performances of the proposed method images from the Martian analogous dataset, the ESA Katwijk Beach Planetary Rover Dataset have been used. The evaluation has been performed by comparing the generated occupancy map with a manually segmented ortomosaic map, obtained by drones' survey of the area used as reference