Today, when we have long entered the era of artificial intelligence, computer vision (CV) could not stay aside from various techniques of machine learning (ML). Moreover, with a rapid progress of deep learning (DL) algorithms and, in particular, convolutional neural networks (CNNs), automatic analysis of visual information is reaching a new level. 

Impact of Machine Learning on Computer Vision

ML and DL methods are now widely used for object detection, recognition and tracking; semantic and instance segmentation; image classification; face detection and recognition; automatic document analysis and OCR; human pose estimation and action recognition; pattern detection and recognition, and many other tasks. But why do CV tasks benefit the most from using machine learning? Many computer vision systems are developed for automatic decision making. Here, the main challenge is to ensure stable operation under changing conditions like indoor and outdoor scenes, varying illumination, occlusions, etc. Even advanced feature extraction and image preprocessing methods often fail when facing such diverse input data. ML algorithms help to tackle the challenges and detect hidden patterns in the available information:

  • Typical unsupervised learning algorithms are used for clustering, dimensionality reduction, and data representation in high-dimensional spaces. This helps a lot in better image/video understanding and interpretation.
  • Supervised ML algorithms help to accumulate various scenarios of visual appearance, which is often impossible to cover using only classical CV pipelines.
  • The most popular DL algorithms (mostly CNNs in CV) have an underlying concept of feature learning instead of feature crafting. This unique advantage of CNNs makes them superior in various CV tasks.

In practice, a combination of traditional CV and ML/DL often gives the best results. 

It-Jim: Machine Learning Consulting and R&D Company

Being experts in both image processing and machine learning, we always combine classical CV and DL to deliver the optimal solution for a given problem based on available hardware resources and infrastructure. For every client, we build a custom methodology, which perfectly meets the requirements and business needs, and ensure the robust performance of ML pipelines in production everywhere: mobile and embedded devices or cloud GPUs.

As a machine learning company, we have run 50+ ML and DL projects and constantly apply the latest achievements and state-of-the-art DL architectures in our research. Here is the list of machine learning and deep learning services that we provide for CV tasks: 

  • machine learning software development
    • dimensionality reduction and data representation (PCA, ICA, LDA)
    • fitting techniques (regression models, splines, OLS, etc.)
    • clustering methods (k-Means, Mean-Shift, EM, DBSCAN, hierarchical clustering, etc.)
    • supervised ML models (SVM, NN, Decision Trees, AdaBoost, k-NN, etc.)
  • advanced feature extraction algorithms
    • feature extraction for image matching (SIFT, SURF, ORB, A-KAZE, d-Nets, edge and line detection methods, HoG, etc.)
    • feature extraction for text localization (SWT, ER, MSER, etc.)
    • feature extraction for faces (Haar, keypoint descriptors, histograms)
    • image preprocessing and filtering (Gabor filtering, wavelets, image thresholding, contrast enhancement, etc.)
  • Deep learning solutions
    • face detection and recognition (openface, RetinaFace, DSFD, etc.)
    • semantic segmentation (U-net, DeepLab, Mask-R-CNN, FastFCN, etc.)
    • monocular depth estimation (DenseDepth, DORN, BTS, etc.)
    • object detection and recognition (YOLO, SSD, RetinaNet, EfficientDet, etc.)
    • human pose estimation (OpenPose, PRM, MSPN, etc.)
    • multiple people tracking (PoseTrack, HRNet,  STAF, etc.)
    • GANs for style transfer, image generation, superresolution, face swap and deep fakes
    • image classification (Inception, ResNet, EfficientNet, etc.)

We often apply our experience in ML/DL to various data processing tasks like time-series analysis and forecasting, text understanding and NLP, voice and speech recognition, audio processing and more. 

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