Classification of Humans into Ayurvedic Prakruti Types using Computer Vision

Ayurveda, a 5000 years old Indian medical science, believes that the universe and hence humans are made up of five elements namely ether, fire, water, earth, and air. The three Doshas (Tridosha) Vata, Pitta, and Kapha originated from the combinations of these elements. Every person has a unique combination of Tridosha elements contributing to a person’s ‘Prakruti’. Prakruti governs the physiological and psychological tendencies in all living beings as well as the way they interact with the environment. This balance influences their physiological features like the texture and colour of skin, hair, eyes, length of fingers, the shape of the palm, body frame, strength of digestion and many more as well as the psychological features like their nature (introverted, extroverted, calm, excitable, intense, laidback), and their reaction to stress and diseases. All these features are coded in the constituents at the time of a person’s creation and do not change throughout their lifetime. Ayurvedic doctors analyze the Prakruti of a person either by assessing the physical features manually and/or by examining the nature of their heartbeat (pulse). Based on this analysis, they diagnose, prevent and cure the disease in patients by prescribing precision medicine. This project focuses on identifying Prakruti of a person by analysing his facial features like hair, eyes, nose, lips and skin colour using facial recognition techniques in computer vision. This is the first of its kind research in this problem area that attempts to bring image processing into the domain of Ayurveda

Tex2Shape: Detailed Full Human Body Geometry From a Single Image

We present a simple yet effective method to infer detailed full human body shape from only a single photograph. Our model can infer full-body shape including face, hair, and clothing including wrinkles at interactive frame-rates. Results feature details even on parts that are occluded in the input image. Our main idea is to turn shape regression into an aligned image-to-image translation problem. The input to our method is a partial texture map of the visible region obtained from off-the-shelf methods. From a partial texture, we estimate detailed normal and vector displacement maps, which can be applied to a low-resolution smooth body model to add detail and clothing. Despite being trained purely with synthetic data, our model generalizes well to real-world photographs. Numerous results demonstrate the versatility and robustness of our method

Tex2Shape: Detailed Full Human Body Geometry From a Single Image

We present a simple yet effective method to infer detailed full human body shape from only a single photograph. Our model can infer full-body shape including face, hair, and clothing including wrinkles at interactive frame-rates. Results feature details even on parts that are occluded in the input image. Our main idea is to turn shape regression into an aligned image-to-image translation problem. The input to our method is a partial texture map of the visible region obtained from off-the-shelf methods. From a partial texture, we estimate detailed normal and vector displacement maps, which can be applied to a low-resolution smooth body model to add detail and clothing. Despite being trained purely with synthetic data, our model generalizes well to real-world photographs. Numerous results demonstrate the versatility and robustness of our method

3D Face Synthesis with KINECT

This work describes the process of face synthesis by image morphing from less expensive 3D sensors such as KINECT that are prone to sensor noise. Its main aim is to create a useful face database for future face recognition studies.Peer reviewe

Can we ID from CCTV? Image quality in digital CCTV and face identification performance

CCTV is used for an increasing number Of purposes, and the new generation of digital systems can be tailored to serve a wide range of security requirements. However, configuration decisions are often made without considering specific task requirements, e.g. the video quality needed for reliable person identification. Our Study investigated the relationship between video quality and the ability of untrained viewers to identify faces from digital CCTV images. The task required 80 participants to identify 64 faces belonging to 4 different ethnicities. Participants compared face images taken from a high quality photographs and low quality CCTV stills, which were recorded at 4 different video quality bit rates (32, 52, 72 and 92 Kbps). We found that the number of correct identifications decreased by 12 (similar to 18%) as MPEG-4 quality decreased from 92 to 32 Kbps, and by 4 (similar to 6%) as Wavelet video quality decreased from 92 to 32 Kbps. To achieve reliable and effective face identification, we recommend that MPEG-4 CCTV systems should be used over Wavelet, and video quality should not be lowered below 52 Kbps during video compression. We discuss the practical implications of these results for security, and contribute a contextual methodology for assessing CCTV video quality

BLOOD & THUNDER CLASSICS, VOL. 2

A MAGAZINE – A game of Chutes and Ladders – a network of pools connected by streams, rivulets, creeks and rivers. Concerns: aluminum, sculpture, film, an endless image or an image-object, cork, shoulders as the center of movement, archery, wicker, nystagmus, darkness or the penumbral near-darkness, constant movement, beer, tone, musical forms, bells, gongs, The Titanic, purple, black and white, indeterminacy, Ghostface, yodeling, John Smith, John Adams, David Hammons, Beyoncé, Honda CR-V’s, Har-khebi, Ahnighito, Hermann Doomer, Prince, Yvonne Rainer, perception, double rainbows, composers from Transylvania, Los Angeles, and chandeliers. “Everything is everything.” and “A woman is the first teacher.

Interactive Vegetation Rendering with Slicing and Blending

Detailed and interactive 3D rendering of vegetation is one of the challenges of traditional polygon-oriented computer graphics, due to large geometric complexity even of simple plants. In this paper we introduce a simplified image-based rendering approach based solely on alpha-blended textured polygons. The simplification is based on the limitations of human perception of complex geometry. Our approach renders dozens of detailed trees in real-time with off-the-shelf hardware, while providing significantly improved image quality over existing real-time techniques. The method is based on using ordinary mesh-based rendering for the solid parts of a tree, its trunk and limbs. The sparse parts of a tree, its twigs and leaves, are instead represented with a set of slices, an image-based representation. A slice is a planar layer, represented with an ordinary alpha or color-keyed texture; a set of parallel slices is a slicing. Rendering from an arbitrary viewpoint in a 360 degree circle around the center of a tree is achieved by blending between the nearest two slicings. In our implementation, only 6 slicings with 5 slices each are sufficient to visualize a tree for a moving or stationary observer with the perceptually similar quality as the original model