Nutrition Facts Recognition from Food Labels Images

In this project we create a diet log application for the consumers based on the their calories intake from different food items. This way the consumer can maintain a regulatory diet and manage accordingly about his/her calories consumption for a day based on the limit set by the consumer itself. This project is implemented in different phases. The first phase includes the image pre-processing which includes median filtering, adaptive thresholding, histogram equalization and segmentation. The second phase includes the optical character recognition on the segmented data. Finally the total calories intake from each food items is added and an alert message is generated to notify the consumer if exceeded day’s limit

Kidney and Kidney Tumor Segmentation using a Logical Ensemble of U-nets with Volumetric Validation

Automated medical image segmentation is a priority research area for computational methods. In particular, detection of cancerous tumors represents a current challenge in this area with potential for real-world impact. This paper describes a method developed in response to the 2019 Kidney Tumor Segmentation Challenge (KiTS19). Axial computed tomography (CT) scans from 210 kidney cancer patients were used to develop and evaluate this automatic segmentation method based on a logical ensemble of fully-convolutional network (FCN) architectures, followed by volumetric validation. Data was pre-processed using conventional computer vision techniques, thresholding, histogram equalization, morphological operations, centering, zooming and resizing. Three binary FCN segmentation models were trained to classify kidney and tumor (2), and only tumor (1), respectively. Model output images were stacked and volumetrically validated to produce the final segmentation for each patient scan. The average F1 score from kidney and tumor pixel classifications was calculated as 0.6758 using preprocessed images and annotations; although restoring to the original image format reduced this score. It remains to be seen how this compares to other solutions.Comment: 9 pages, 4 figures, 1 table, competition submission manuscrip

Color Palette menggunakan Python cv2 dan NumPy

Python cv2 dan numPy merupakan perpustakaan fungsi pemograman yang ditujukan untuk visi komputer waktu nyata. Cv2 sebagai library untuk pengolahan citra objek secara umum baik objek manusia maupun objek kotak, numPy sebagai library untuk komputasi dan perhitungan sehingga dengan mengkolaborasikan library ini diperoleh proses deteksi objek citra dengan proses iterasi menggunakan numerical lebih cepat dan akurat. Penelitian ini menggunakan metode eksperimental dimana akan dibuat jendela yang berisi palet warna RGB dengan trackbar menggunakan kolaborasi fungsi dari perpustakaan cv2 dan numPy. Dengan memindahkan trackbar, nilai warna RGB akan berubah warna pada rentang 0 hingga 255 dengan nilai heksadesimal sesuai dengan standar ISO 20677

“A Review on Design and Implementation of Image Enhancement for Underwater Image”

For the Underwater image, currently the gathered images have different grades of distortion and wrong information due to the influence of underwater special environment. There are two basic process for light propagation in the sea water; Absorption and disperses .The process of the light in water can affect the overall performance of underwater imaging system. The above characteristics lead to uneven illumination, low contrast of image and poor quality of the image. For underwater images de noising, a new method based on adaptive wavelet is proposed. Finally the simulation results show that the proposed work not only eliminate the noise effectively but also improves image output peak signal-to-noise ratio (PSNR).Then after Enhancement algorithms which is generally interactive and application dependent is to bring out detail that is hidden part in an image is covered up in a picture or to increases contrast in a low contrast picture. Image enhancement is useful in feature information, image study and visual information display. It simply emphasizes certain specified image characteristics. In this paper an efficient image enhancement algorithm i.e., pre processing, thresholding, contrast adjustment, power law transform is implemented on Spartan -3 FPGA

Kidney and Kidney Tumor Segmentation using a Logical Ensemble of U-nets with Volumetric Validation

Automated medical image segmentation is a priority research area for computational methods. In particular, detection of cancerous tumors represents a current challenge in this area with potential for real-world impact. This paper describes a method developed in response to the 2019 Kidney Tumor Segmentation Challenge (KiTS19). Axial computed tomography (CT) scans from 210 kidney cancer patients were used to develop and evaluate this automatic segmentation method based on a logical ensemble of fully-convolutional network (FCN) architectures, followed by volumetric validation. Data was pre-processed using conventional computer vision techniques, thresholding, histogram equalization, morphological operations, centering, zooming and resizing. Three binary FCN segmentation models were trained to classify kidney and tumor (2), and only tumor (1), respectively. Model output images were stacked and volumetrically validated to produce the final segmentation for each patient scan. The average F1 score from kidney and tumor pixel classifications was calculated as 0.6758 using preprocessed images and annotations; although restoring to the original image format reduced this score. It remains to be seen how this compares to other solutions

Preliminary study

The concept of Precision Agriculture is usually associated with the usage of high-end technology equipment (hardware or software) to evaluate or monitor the conditions of a determined portion of land, adjusting afterwards the production factors, like seeds, fertilizers, pesticides, growing regulators, water, according to differential detected characteristics. This paper describes an algorithm developed to analyze and process images to recognize fruits, particularly peaches, and calculate it dimensions, like volume and weight. The recognition of peaches on their natural conditions on trees depends on several spatial- and time-variable parameters and requires complex segmentation algorithms. The proposed algorithm applies image segmentation for extraction of characteristics such as color and shape. These characteristics were used to train a classification method through a Support Vector Machine (SVM) to improve the recognition rate of fruits. The algorithm is designed to acquire images with a high-resolution camera installed in a drone that will fly between the tree lines. The production prediction of 29.3 tons per hectare was obtained based on volume and relation weight/volume calculated for the recognized peaches. An overall precision of 72% was achieved for the prediction rate of peaches in orchards (808 trees/ha). This is the first study regarding the application of these concepts under orchard trees aiming the production prediction along the fruit maturation. Other useful future applications are foreseen in orchard trees, related not only to production prediction, for this type of algorithm.Project "PrunusBOT – Sistema robótico aéreo autónomo de pulverização controlada e previsão de produção frutícola", n.º PDR2020-101-031358, funded by Rural Development Program of the Portuguese Government - Programa de Desenvolvimento Rural (PDR 2020), Portugal 2020.info:eu-repo/semantics/publishedVersio