jackchen69's Stars
Jiaxin-Ye/TIM-Net_SER
[ICASSP 2023] Official Tensorflow implementation of "Temporal Modeling Matters: A Novel Temporal Emotional Modeling Approach for Speech Emotion Recognition".
cecca46/MentalDisorderRecogntion
skeletonNN/NHFNet
X-PLUG/mPLUG-Owl
mPLUG-Owl & mPLUG-Owl2: Modularized Multimodal Large Language Model
ProfBressan/FeatureExtraction
Feature extraction - Image Processing
chsasank/image_features
Extract deep learning features from images using simple python interface
christiansafka/img2vec
:fire: Use pre-trained models in PyTorch to extract vector embeddings for any image
PaddlePaddle/PaddleHub
Awesome pre-trained models toolkit based on PaddlePaddle. (400+ models including Image, Text, Audio, Video and Cross-Modal with Easy Inference & Serving)
keplerlab/katna
Tool for automating common video key-frame extraction, video compression and Image Auto-crop/Image-resize tasks
TejasBob/Panorama
Image Stitching on key-frames extracted from video
freearhey/face-extractor
Python script that detect faces on the image or video, extracts them and saves to the specified folder
miguelcecci/DNN-Face-Extraction
Extract faces from video and save as jpg
jainsee24/Parallel-Face-detection
Image segmentation is the process of dividing an image into multiple parts. It is typically used to identify objects or other relevant information in digital images. There are many ways to perform image segmentation including Thresholding methods, Color-based segmentation, Transform methods among many others. Alternately edge detection can be used for image segmentation and data extraction in areas such as image processing, computer vision, and machine vision. Image thresholding is a simple, yet effective, way of partitioning an image into a foreground and background. This image analysis technique is a type of image segmentation that isolates objects by converting grayscale images into binary images. Image thresholding is most effective in images with high levels of contrast. Otsu's method, named after Nobuyuki Otsu, is one such implementation of Image Thresholding which involves iterating through all the possible threshold values and calculating a measure of spread for the pixel levels each side of the threshold, i.e. the pixels that either fall in foreground or background. The aim is to find the threshold value where the sum of foreground and background spreads is at its minimum. Edge detection is an image processing technique for finding the boundaries of objects within images. It works by detecting discontinuities in brightness. An image can have horizontal, vertical or diagonal edges. The Sobel operator is used to detect two kinds of edges in an image by making use of a derivative mask, one for the horizontal edges and one for the vertical edges. 1. Introduction Face detection is a computer technology being used in a variety of applications that identifies human faces in digital images. Face detection also refers to the psychological process by which humans locate and attend to faces in a visual scene. Face detection can be regarded as a specific case of object-class detection. In object-class detection, the task is to find the locations and sizes of all objects in an image that belong to a given class. Examples include upper torsos, pedestrians, and cars. Face-detection algorithms focus on the detection of frontal human faces. It is analogous to image detection in which the image of a person is matched bit by bit. Image matches with the image stores in database. Any facial feature changes in the database will invalidate the matching process. 2. Needs/Problems There have been widely applied many researches related to face recognition system. The system is commonly used for video surveillance, human and computer interaction, robot navigation, and etc. Along with the utilization of the system, it leads to the need for a faster system response, such as robot navigation or application for public safety. A number of classification algorithms have been applied to face recognition system, but it still has a problem in terms of computing time. In this system, computing time of the classification or feature extraction is an important thing for further concern. To improve the algorithmic efficiency of face detection, we combine the eigenface method using Haar-like features to detect both of eyes and face, and Robert cross edge detector to locate the human face position. Robert Cross uses the integral image representation and simple rectangular features to eliminate the need of expensive calculation of multi-scale image pyramid. 3. Objectives Some techniques used in this application are 1. Eigen-face technique 2. KLT Algorithm 3. Parallel for loop in openmp 4. OpenCV for face detection. 5. Further uses of the techniques
ChangLabUcsf/face_extraction
get facial feature data from video
valiakon/MultimodalAnalysis_SpeakerDiarization
The project tries to solve a speaker diarization problem using audio features, face recognition and video feature extraction from face image, mouth tracking.
DevendraPratapYadav/face-extraction-from-video
Extract cropped face video segments from an input video using OpenCV, Python
joelibaceta/video-keyframe-detector
It is a simple python tool to extract key-frames from a video file using peak estimation from frame difference.
lucidrains/vit-pytorch
Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch
djordjebatic/AffectNet
jeehyunee3/multi_modal_based_video_emotion_classification
In this study, we propose a multimodal approach for emotion recognition in video data by extracting audio and images using pre-trained VGG and ViT models.
SwinTransformer/Video-Swin-Transformer
This is an official implementation for "Video Swin Transformers".
haofanwang/video-swin-transformer-pytorch
Video Swin Transformer - PyTorch
facebookresearch/segment-anything
The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.
XiaoYee/emotion_classification
facial expression recognition with Pytorch
antoine77340/video_feature_extractor
Easy to use video deep features extractor
v-iashin/video_features
Extract video features from raw videos using multiple GPUs. We support RAFT flow frames as well as S3D, I3D, R(2+1)D, VGGish, CLIP, and TIMM models.
NetoPedro/Face-Verification-VGGFace
Face recognition model trained on VGG Faces 2 to recognise people on videos without being explicitly trained on them. (Based on a database of people pictures can identify the person of the video through the extracted features)
aaronpp65/face-recognition-vggface2
The VGGFace refers to a series of models developed for face recognition and demonstrated on benchmark computer vision datasets by members of the Visual Geometry Group (VGG) at the University of Oxford.
nddbk/vgg-faces-utils
Script to download and annotate images from VGG Faces dataset
cydonia999/VGGFace2-pytorch
PyTorch Face Recognizer based on 'VGGFace2: A dataset for recognising faces across pose and age'