Dietary choices have a substantial impact on the health of an individual. This AI-driven research aims to recognize, classify, and estimate the origin and nutrition of food. The proposed system is trained using a diverse dataset containing images of food (101 Classes) in different lights and environmental conditions. In this research a transfer learning approach applied with ResNet and InceptionV3 architectures using their pre-trained weights with finetuning of hyperparameters (Learning rate, Batch size and Optimizer). As a result of this approach, the ability to learn intricate features relevant to food recognition was retained while training rapidly. The system achieves impressive accuracy: 96.6% and 96.1% respectively for food identification, nutrient, and origin estimation. The system accurately recognizes popular foods like pizza, sushi, and salads, even in low light. Furthermore, to provide reliable food information to end users, we have developed a user-friendly web application. The app allows users to upload pictures of their meals to receive nutritional and origin information, empowering them to make healthier choices. This simplifies the process of making informed dietary choices for individuals.
Pencil drawing is an important artistic style which is widely used in draft sketch and finished rendering. It is non-trivial if we can automatically generate the pencil drawing more precisely in order to ease the human efforts. In this paper, we propose flow-guided sketch and accentuated-tone adjusting techniques to transform the pencil drawing from natural images. This work is extended from the existing pencil drawing framework where authors combine sketch and tone together for penciling purpose. Through an edge tangent flow guided classification and convolution, we can obtain more coherent and smooth strokes, especially for portraits. In addition, we also introduce a hierarchical approach to generate strokes. According to saliency map, we can adjust the tone of images automatically. The experimental results indicate the generated toned pencil drawings by our approach are of fewer artifacts and could even solve the images with dark background
This paper proposes a fast and accurate algorithm for indirect illumination. It uses volumes of different resolutions to sample and cache the geometric information and the secondary lights. By dividing the irradiance into two parts, it treats the lights coming from the far-field and that coming from the near-field differently. For the far-field ones, it propagates sphere harmonic represented lights on coarse voxels. For the near-field ones, it shoots rays and collects their contributions on fine voxels. By doing this, the algorithm in this paper avoids using many rays to march long distance. In the experiments, it renders about ten times faster than the VGI algorithm to get the same image qualities, especially for the large and complex scenes. Meanwhile, it further accelerates the rendering by inventing an incremental multi-resolution gathering. The experiments illustrate fast and accurate indirect light effects.
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