Indian Institute of Information Technology, Allahabad

Computer Vision and Biometrics Lab (CVBL)

Visual Recognition

July-Dec 2025 Semester


Previous Offerings: 2021, 2022, 2023, 2024


Course Information

Objective of the course: The field of visual recognition has become part of our lives with applications in self-driving cars, satellite monitoring, surveillance, video analytics particularly in scene understanding, crowd behaviour analysis, action recognition etc. It has eased human lives by acquiring, processing, analyzing and understanding digital images and extraction of high-dimensional data from the real world in order to produce numerical or symbolic information. The visual recognition encapsulates image classification, localization and detection. The course on visual recognition will help students understand new tools, techniques and methods which are influencing the visual recognition field.

Outcome of the course: At the end of this course, the students will be able apply the concepts to solve some real problems in recognition. The students will be able to use computational visual recognition for problems ranging from extracting features, classifying images, to detecting and outlining objects and activities in an image or video using machine learning and deep learning concepts. The student will be also being able to invent new methods in visual recognition for various applications.



Class meets
Tuesday: 8:50 - 10:50 and Friday: 9:50 - 10:50

Course Ethics
  • Students are strictly advised to avoid the unethical practices in the course including review tests and practice components.
  • The project component will be done in team. The team will be formed by the course instructors. The project allotment will be also done by the course instructors.
  • Students are not allowed to simply claim the existing solutions available in public domain as your own work in this course.
  • If it happens that you have already done the similar projects in any other course or with any other faculty which is allotted to you, you should immediately inform us for the same as it is not allowed to have similar projects in this course which you might have already done previously.
  • It is best to try to solve problems on your own, since problem solving is an important component of the course.
  • You are not allowed to do or continue same project in any other course and with any other faculty.
  • You are allowed to discuss class material, problems, and general solution strategies with your classmates. But, when it comes to formulating or writing solutions you must work/implement by yourself.
  • You may use free and publicly available sources, such as books, journal and conference publications, and web pages, as research material for your answers. (You will not lose marks for using external sources.) It is does not mean that you claim these existing resources as your work.
  • You may not use any paid service and you must clearly and explicitly cite all outside sources and materials that you made use of.
  • Students are not allowed to post the code/report/any other material of course project in public domain or share with any one else without written permission from course instructors.
  • We consider the use of uncited external sources as portraying someone else's work as your own, and as such it is a violation of the Institute's policies on academic dishonesty.
  • Instances will be dealt with harshly and typically result in a failing course grade.

Schedule

Schedule Topic Resources
L01:Course Introduction Slide
L02:Feature Extraction Slide
L03:Image Categorization Slide
L04:Image Classifiers Slide
L05:Neural Networks Slide
L06:Convolutional Neural Networks Slide
L07:CNN Training 1 Slide
L08:CNN Training 2 Slide
L09:CNN Architectures Slide
L10:Object Detection Slide
L11:Semantic Segmentation Slide
L12:Adversarial Attack Slide
L13:Generative Models Slide
L14:Diffusion Models Slide
L15:Transformer Models Slide
L16:Video Recognition Slide
L17:Explaining CNNs Slide

Computational Projects Added to Teaching Laboratories

Project ID Team Project Title Abstract
VR25-P01 IFI2022002 ASHARAM MEENA, IFI2022012 HARSH, IIT2022022 DIMPLE BHONDEKAR, IIT2022038 TRILOK MEENA Detection and reconstruction of damaged/eroded regions in scanned old documents
VLR25-P02 IFI2022007 VERSHA ARYA, IIT2022076 PRIYAL SINGH, IIT2022088 SRUNGAVARAPU BRAMARA MALLESWARI, IIT2022096 EGIREDDI JYOTHIRMAYI Vision based answer sheet evaluation
VR25-P03 IFI2022019 SHUBHAM GUPTA, IFI2022024 ABHAY TIWARI, IIT2022007 SAMUEL E GEORGE, IIT2022058 ASHER AHMAD Handwritten Math Expression Recognition
VR25-P04 IFI2022022 URITI JASHWANTH BALA, IIT2022153 ANKIT GAUTAM, IIT2022200 VADTHYA VENKATESH, IIT2022274 TEJKUMAR BIJARNIYA Disguised Faces Recognition
VLR25-P05 IIT2022056 SWEETY SOLANKI, IIT2022120 SNEHA JAISWAL, IIT2022208 ANANYA GARG, IIT2022219 DINESH PRADHAN Micro expression recognition
VR25-P06 IIT2022014 ARPIT GUPTA, IIT2022124 CHIRAG PALIWAL, IIT2022150 TOHID KHAN, IIT2022191 CHANDA SAI GANESH Detection and Classification of Road Pavements in Images
VR25-P07 IIT2022063 KOVID KUMAR JUYAL, IIT2022071 LALDUHKIMA, IIT2022101 SHUBHAM MISHRA, IIT2022149 HARSHAD BANGADKAR Thermal to visible image translation under occluded conditions
VR25-P08 IIT2022001 DEVIKA O NAIR, IIT2022024 ARYAN RASIWASIA, IIT2022045 VIPUL KUMAR, IIT2022099 TAMMINA MOULYA Rotational Face Pose Correction
VR25-P09 IIT2022027 GORANSH BARDE, IIT2022047 ANAND RAJ, IIT2022065 PRADIP DATTATRAY YADAV, IIT2022103 GAJULAPALLI SAI VARSHITA Classification of 2D and 3D objects based on images
VR25-P10 IIT2022028 GAURAV SINGH PAINKARA, IIT2022077 VAKADA MARTIN, IIT2022097 MUDDADA DHATRI SRI, IIT2022171 MADASU RAKESH Recognition of different plant species around IIITA campus
VR25-P11 IIT2022032 NARAYANAN AGATSYA PURUSHOTTAM, IIT2022072 GOVIND ANAND, IIT2022109 SHOBHIT KULSHRESTHA, IIT2022181 BATHULA HRISHITHA Fashion Outfit Retrieval
VR25-P12 IIT2022049 AVANISH GURJAR, IIT2022084 HARSHINI BHUKYA, IIT2022199 MENAVATH MEGHANA, IIT2022225 PRATTIPATI ABHINAV KARTHIK Classification of waste as degradable/non-degradable
VR25-P13 IIT2022090 AYUSH KUMAR, IIT2022174 ANKIT RAJ, IIT2022224 BANALA NEHA, IIT2022273 TALASILA LOKESH Image to Image translation for day and night images of IIITA campus
VR25-P14 IIT2022050 AMAN RAJ, IIT2022075 ABHIMANYU CHOUDHARY, IIT2022139 YUVRAJ KUMAR, IIT2022196 CHAGAMU KAVYA 3D Object Reconstruction using multiple 2D images
VR25-P15 IIT2022087 PEDAPALLI JAMES, IIT2022202 SARANGAPANI AKSHITHA, IIT2022229 KATARU RAM CHARAN, IIT2022265 SHUBHAM SINGH SAINI Real-Time Vehicle Type Classification using shape and size
VR25-P16 IIT2022051 PIYUSH PRIYADARSHI, IIT2022106 BOMMAKANTI ABHINAV, IIT2022203 AARAV SHRIVASTAV, IIT2022248 THIRUNAGARI SRI KUMAR Identification and classification of waste and non-waste material in daily life use
VR25-P17 IIT2022095 CHIRUMAMILLA SAI SRI PRAGHNA, IIT2022121 KSHITIJ BUDHE, IIT2022223 AKSHITHA BITLA, IIT2022262 SIDDHARTH YADAORAO SHAHARE Dog Detection in campus area
VR25-P18 IIT2022055 BHOYAREKAR ADITYA KRUSHNA, IIT2022108 SIDDHARTH WASKLE, IIT2022214 SANJANA PRAJAPATI, IIT2022237 HIMANSHU RAJ Underwater image enhancement
VR25-P19 IIT2022098 YAGESH MISHRA, IIT2022129 ATHARAV BHANUDAS YADAV, IIT2022212 RATHLAVATH VINOD, IIT2022267 DIVYANSHU MADHAV Calorie Estimator and Food Image Classification
VR25-P20 IIT2022209 TANNIRU NAVEEN, IIT2022227 RAJAT, IIT2022239 MUNISH KUMAR, IIT2022269 ATTADA PRANEETH Detection of helmet on bike riders entering campus
VR25-P21 IIT2022100 RAHUL KUMAR, IIT2022185 BATCHU MEENAKSHI, IIT2022216 ANSH GADWAL, IIT2022257 KUSHAGRA PATEL Genre classification of videos based on deep learning approach
VR25-P22 IIT2022217 DEVESH, IIT2022230 THOTA SIDDHARTHA, IIT2022259 ANKIT KUMAR, IIT2022270 MITANSHU GAURAV Object detection in night vision
VR25-P23 IIT2022222 ANKIT PRAKASH, IIT2022231 LAXMI NARAYAN SHARMA, IIT2022264 DHORIYANI AAKASH ASHVINKUMAR Custom Plant disease detection and classification
VR25-P24 PMM2024002 S GOKUL RAJ Detection of optimal empty space for a certain type of vehicle in parking spaces
VR25-P25 RSI2024504 DEEKSHA GUPTA Explainable Cancer classification from Histopathology

Grading

Prerequisites

Books

Disclaimer

The content (text, image, and graphics) used in this slide are adopted from many sources for Academic purposes. Broadly, the sources have been given due credit appropriately. However, there is a chance of missing out some original primary sources. The authors of this material do not claim any copyright of such material.