In this paper, we propose a novel hybrid transformer architecture for food cuisine detection and classification. The work carried out within this paper develops a combination of Vision Transformer ensemble architecture with hand-crafted features, thereby making a hybrid Vision Transformer food recognition system. Recently, Vision transformers have been introduced as an alternative means of classification to convolutional neural networks. It performs pattern detection and classification without convolutions and interprets an image as a sequence of patches. The combination of Vision Transformer and hand-crafted features like GIST, HoG (Histogram of Oriented Gradients), and LBP (Local Binary Pattern) were employed on the dataset. The dataset was specifically created (for this work) from the public logging system. It consisted of 13 food categories with 400 images of Indian food items like Ghevar, Idli, Dosa, and much more. It helped to capture a variety of images from every domain and cul
Objective: Parkinson's disease (PD) is a common neurological disorder with variable clinical manifestations and magnetic resonance imaging (MRI) findings. We propose a handcrafted image classification model that can accurately (i) classify different PD stages, (ii) detect comorbid dementia, and (iii) discriminate PD-related motor symptoms. Methods: Selected image datasets from three PD studies were used to develop the classification model. Our proposed novel automated system was developed in four phases: (i) texture features are extracted from the non-fixed size patches. In the feature extraction phase, a pyramid histogram-oriented gradient (PHOG) image descriptor is used. (ii) In the feature selection phase, four feature selectors: neighborhood component analysis (NCA), Chi2, minimum redundancy maximum relevancy (mRMR), and ReliefF are used to generate four feature vectors. (iii) Two classifiers: k-nearest neighbor (kNN) and support vector machine (SVM) are used in the classifica
This paper shows different Pummel classifications that can currently be found on the market. Subsequently, approaches to the automatization and standardization of the execution and especially the evaluation of the Pummel test are shown.