00:39

Does SegmentAnything segment anything?

01:22

The One Minute Movie Mistake computer vision workflow

04:11

High contrast and color-coded warehouse design for drones

02:56

Landing Gear Segmentation

01:51

Environment segmentation: reflections

02:04

Fast depth adjustment (part 1)

02:40

Depth of Field Visualized

01:35

Do details matter? (A quick test)

00:45

Shadow segmentation attempt

01:03

Torching twin (color-based segmentation)

01:18

Environmental tracking and monitoring of critical objects

02:30

Actionable digital twins

01:15

Image generation based on segmentation results

03:17

Indoor motion tracking

05:18

Estimated depth stabilization

02:28

Will vectorised details help digital twins?

03:44

Monocular depth meets Arcane (part 1)

01:05

A single shape describes the scene

09:16

Integrated segmentation environment

01:00

Moose shedding antlers

02:03

Color-based segmentation and non-ideal lighting conditions

02:00

Complex forms (color-based segmentation)

03:46

A simulated crash test detailed segmentation

01:25

Estimated monocular depth issues

00:57

Surface reconstruction based on estimated monocular depth

03:53

Table tennis digital twin concept

00:38

Custom segmentation will always be required (a brief demo)

01:36

A quick attempt to build a crash-test digital twin (Attempt 1)

00:28

Landscape semantic segmentation (Attempt 1)

00:39

The Matrix Red dress segmentation benchmark (with instance segmentation attempt)

06:00

Motion tracking in a complex environment

01:20

First attempt to track crash-test markers

04:33

Estimated monocular depth comparison

01:41

Round shapes are everywhere (Attempt 02)

01:08

Rough car 3D position using a single wheel (JFF exercise)

01:15

Use drones to track animals (Attempt 1)

01:13

Tiny but complex details as a segmentation benchmark (Attempt 1)

00:36

Specific process tracking (Attempt 1)

01:20

Fox hunting (Attempt 2)

01:12

Combining huge and tiny objects (Attempt 1)

01:03

Very first attempt to deal with agriculture drone footage (Attempt 1)

00:56

Makeup process tracking (Attempt 1)

01:44

A quick attempt to make a car review personalized in terms of color (Attempt 1)

01:59

Fox hunting (attempt 1)

00:56

Road and moving car segmentation (attempt 1)

01:33

Estimated monocular depth helps with baja truck segmentation

01:31

Tiny details get properly segmented with a resolution boost feature

00:55

Flying ground-effect model as a complex object/process example (attempt 1)

00:17

Detailed geometry of a plane and nearest "markers"

00:28

Serviceman working (color-based segmentation)

02:43

Detailed multi-layer flower segmentation

00:42

Baby shark color-based segmentation (including shadow)

00:32

Yet another shiny depth of field

00:17

Wasp close up segmentation (initial attempt)

00:30

Color noise visualization

01:28

Is it enough to have only segmentation results for any decision making?

02:12

Semantic segmentation of a plane graveyard (including shadow)

00:30

DICOM image segmentation

01:01

Precise semantic segmentation of a flower (color-based)

00:44

Initial segmentation results for a custom training set (flowers)